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RAG and LoRa setup — Part 2

CONVERSATIONS · THE PRESERVED RECORD

A question.
A conversation worth keeping.

The original exchange, kept in context—ready to read, revisit, and explore.

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Darren2025-09-19T21:24:56-07:00

darren@ougway:~/ougway_env/llama.cpp$ /home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
-m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
-ngl 999 -c 32768 -b 16 -t $(nproc) -n 400 –jinja \
-f big32k.txt
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 6511 (4ca088b0) with cc (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060) (0000:01:00.0) – 11573 MiB free
llama_model_loader: loaded meta data with 28 key-value pairs and 399 tensors from /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: – kv 0: general.architecture str = qwen3
llama_model_loader: – kv 1: general.type str = model
llama_model_loader: – kv 2: general.name str = Qwen3 8B Instruct
llama_model_loader: – kv 3: general.finetune str = Instruct
llama_model_loader: – kv 4: general.basename str = Qwen3
llama_model_loader: – kv 5: general.size_label str = 8B
llama_model_loader: – kv 6: qwen3.block_count u32 = 36
llama_model_loader: – kv 7: qwen3.context_length u32 = 40960
llama_model_loader: – kv 8: qwen3.embedding_length u32 = 4096
llama_model_loader: – kv 9: qwen3.feed_forward_length u32 = 12288
llama_model_loader: – kv 10: qwen3.attention.head_count u32 = 32
llama_model_loader: – kv 11: qwen3.attention.head_count_kv u32 = 8
llama_model_loader: – kv 12: qwen3.rope.freq_base f32 = 1000000.000000
llama_model_loader: – kv 13: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: – kv 14: qwen3.attention.key_length u32 = 128
llama_model_loader: – kv 15: qwen3.attention.value_length u32 = 128
llama_model_loader: – kv 16: tokenizer.ggml.model str = gpt2
llama_model_loader: – kv 17: tokenizer.ggml.pre str = qwen2
llama_model_loader: – kv 18: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", …
llama_model_loader: – kv 19: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
llama_model_loader: – kv 20: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",…
llama_model_loader: – kv 21: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: – kv 22: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: – kv 23: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: – kv 24: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: – kv 25: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>…
llama_model_loader: – kv 26: general.quantization_version u32 = 2
llama_model_loader: – kv 27: general.file_type u32 = 7
llama_model_loader: – type f32: 145 tensors
llama_model_loader: – type q8_0: 254 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 8.11 GiB (8.50 BPW)
load: printing all EOG tokens:
load: – 151643 ('<|endoftext|>')
load: – 151645 ('<|im_end|>')
load: – 151662 ('<|fim_pad|>')
load: – 151663 ('<|repo_name|>')
load: – 151664 ('<|file_sep|>')
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3
print_info: vocab_only = 0
print_info: n_ctx_train = 40960
print_info: n_embd = 4096
print_info: n_layer = 36
print_info: n_head = 32
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 4
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 12288
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 40960
print_info: rope_finetuned = unknown
print_info: model type = 8B
print_info: model params = 8.19 B
print_info: general.name = Qwen3 8B Instruct
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while… (mmap = true)
load_tensors: offloading 36 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 37/37 layers to GPU
load_tensors: CUDA0 model buffer size = 7669.77 MiB
load_tensors: CPU_Mapped model buffer size = 630.59 MiB
……………………………………………………………………………
llama_context: constructing llama_context
llama_context: n_batch is less than GGML_KQ_MASK_PAD – increasing to 64
llama_context: n_seq_max = 1
llama_context: n_ctx = 32768
llama_context: n_ctx_per_seq = 32768
llama_context: n_batch = 64
llama_context: n_ubatch = 64
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_per_seq (32768) < n_ctx_train (40960) — the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
ggml_backend_cuda_buffer_type_alloc_buffer: allocating 4608.00 MiB on device 0: cudaMalloc failed: out of memory
alloc_tensor_range: failed to allocate CUDA0 buffer of size 4831838208
llama_init_from_model: failed to initialize the context: failed to allocate buffer for kv cache
common_init_from_params: failed to create context with model '/home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf', try reducing –n-gpu-layers if you're running out of VRAM
main: error: unable to load model
darren@ougway:~/ougway_env/llama.cpp$

ChatGPT2025-09-19T21:24:59-07:00 · gpt-5-thinking

Boom — we hit the exact ceiling I expected: the KV cache doubled when you went from 16k → 32k and pushed VRAM over 12 GB.

From your 16k run:
– Model buffer on GPU: ~7.67 GiB
– KV cache: ~2.304 GiB (reported)
– At 32k, KV ≈ 2 × 2.304 GiB = ~4.6 GiB → 7.67 + 4.6 + overhead > 12 → OOM.

You’ve got three clean ways to fix it—pick one:

Option A — Use a slightly smaller context (easy & fast)

32k is just a bit too tall. 24k–28k will fit.

  • 24k ctx (~3.456 GiB KV): safe
/home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
  -ngl 999 -c 24576 -b 16 -t $(nproc) -n 400 --jinja -f big32k.txt
  • 28k ctx (~4.032 GiB KV): probably OK but tighter
/home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
  -ngl 999 -c 28672 -b 16 -t $(nproc) -n 400 --jinja -f big32k.txt

Option B — Keep 32k, free VRAM by moving a few layers to CPU

Lower -ngl so the GPU model buffer shrinks ~0.8–1.2 GiB. It’ll be a bit slower, but you get 32k.

/home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
  -ngl 30 -c 32768 -b 8 -t $(nproc) -n 400 --jinja -f big32k.txt

Notes:
– -ngl 30 (instead of 999) leaves ~6 layers on CPU, freeing ~VRAM.
– -b 8 trims compute buffers a bit more.

Option C — Keep all layers on GPU, swap to Q6_K quant

Q6_K drops the GPU model buffer by ~1 GiB vs Q8_0, giving room for 32k without moving layers.

1) Download Q6_K file (one-liner):

hf download Qwen/Qwen3-8B-GGUF Qwen3-8B-Q6_K.gguf --local-dir ~/Downloads
mv ~/Downloads/Qwen3-8B-Q6_K.gguf /home/darren/ougway_env/llama.cpp/models/qwen3/

2) Run:

/home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q6_K.gguf \
  -ngl 999 -c 32768 -b 16 -t $(nproc) -n 400 --jinja -f big32k.txt

Quick sanity math (so it’s intuitive next time)

KV cache scales ~linearly with context:
– 16k → ~2.3 GiB
– 24k → ~3.5 GiB
– 28k → ~4.0 GiB
– 32k → ~4.6 GiB ⟵ this plus ~7.7 GiB model = OOM on 12 GB

If you want the simplest path tonight: try -c 24576 first (Option A). If you really want 32k, go with Option B (-ngl 30, -b 8) or Option C (Q6_K).

Darren2025-09-19T21:26:25-07:00

darren@ougway:~/ougway_env/llama.cpp$ /home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
-m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
-ngl 999 -c 24576 -b 16 -t $(nproc) -n 400 –jinja -f big32k.txt
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 6511 (4ca088b0) with cc (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060) (0000:01:00.0) – 11576 MiB free
llama_model_loader: loaded meta data with 28 key-value pairs and 399 tensors from /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: – kv 0: general.architecture str = qwen3
llama_model_loader: – kv 1: general.type str = model
llama_model_loader: – kv 2: general.name str = Qwen3 8B Instruct
llama_model_loader: – kv 3: general.finetune str = Instruct
llama_model_loader: – kv 4: general.basename str = Qwen3
llama_model_loader: – kv 5: general.size_label str = 8B
llama_model_loader: – kv 6: qwen3.block_count u32 = 36
llama_model_loader: – kv 7: qwen3.context_length u32 = 40960
llama_model_loader: – kv 8: qwen3.embedding_length u32 = 4096
llama_model_loader: – kv 9: qwen3.feed_forward_length u32 = 12288
llama_model_loader: – kv 10: qwen3.attention.head_count u32 = 32
llama_model_loader: – kv 11: qwen3.attention.head_count_kv u32 = 8
llama_model_loader: – kv 12: qwen3.rope.freq_base f32 = 1000000.000000
llama_model_loader: – kv 13: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: – kv 14: qwen3.attention.key_length u32 = 128
llama_model_loader: – kv 15: qwen3.attention.value_length u32 = 128
llama_model_loader: – kv 16: tokenizer.ggml.model str = gpt2
llama_model_loader: – kv 17: tokenizer.ggml.pre str = qwen2
llama_model_loader: – kv 18: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", …
llama_model_loader: – kv 19: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
llama_model_loader: – kv 20: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",…
llama_model_loader: – kv 21: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: – kv 22: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: – kv 23: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: – kv 24: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: – kv 25: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>…
llama_model_loader: – kv 26: general.quantization_version u32 = 2
llama_model_loader: – kv 27: general.file_type u32 = 7
llama_model_loader: – type f32: 145 tensors
llama_model_loader: – type q8_0: 254 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 8.11 GiB (8.50 BPW)
load: printing all EOG tokens:
load: – 151643 ('<|endoftext|>')
load: – 151645 ('<|im_end|>')
load: – 151662 ('<|fim_pad|>')
load: – 151663 ('<|repo_name|>')
load: – 151664 ('<|file_sep|>')
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3
print_info: vocab_only = 0
print_info: n_ctx_train = 40960
print_info: n_embd = 4096
print_info: n_layer = 36
print_info: n_head = 32
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 4
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 12288
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 40960
print_info: rope_finetuned = unknown
print_info: model type = 8B
print_info: model params = 8.19 B
print_info: general.name = Qwen3 8B Instruct
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while… (mmap = true)
load_tensors: offloading 36 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 37/37 layers to GPU
load_tensors: CUDA0 model buffer size = 7669.77 MiB
load_tensors: CPU_Mapped model buffer size = 630.59 MiB
……………………………………………………………………………
llama_context: constructing llama_context
llama_context: n_batch is less than GGML_KQ_MASK_PAD – increasing to 64
llama_context: n_seq_max = 1
llama_context: n_ctx = 24576
llama_context: n_ctx_per_seq = 24576
llama_context: n_batch = 64
llama_context: n_ubatch = 64
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_per_seq (24576) < n_ctx_train (40960) — the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CUDA0 KV buffer size = 3456.00 MiB
llama_kv_cache: size = 3456.00 MiB ( 24576 cells, 36 layers, 1/1 seqs), K (f16): 1728.00 MiB, V (f16): 1728.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context: CUDA0 compute buffer size = 38.09 MiB
llama_context: CUDA_Host compute buffer size = 7.00 MiB
llama_context: graph nodes = 1267
llama_context: graph splits = 2
common_init_from_params: added <|endoftext|> logit bias = -inf
common_init_from_params: added <|im_end|> logit bias = -inf
common_init_from_params: added <|fim_pad|> logit bias = -inf
common_init_from_params: added <|repo_name|> logit bias = -inf
common_init_from_params: added <|file_sep|> logit bias = -inf
common_init_from_params: setting dry_penalty_last_n to ctx_size = 24576
common_init_from_params: warming up the model with an empty run – please wait … (–no-warmup to disable)
main: llama threadpool init, n_threads = 8
main: chat template is available, enabling conversation mode (disable it with -no-cnv)
*** User-specified prompt will pre-start conversation, did you mean to set –system-prompt (-sys) instead?
main: chat template example:
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant

system_info: n_threads = 8 (n_threads_batch = 8) / 8 | CUDA : ARCHS = 500,610,700,750,800,860,890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |

main: prompt is too long (422090 tokens, max 24572)
darren@ougway:~/ougway_env/llama.cpp$

ChatGPT2025-09-19T21:26:27-07:00 · gpt-5-thinking

Ah—your prompt file was way bigger than the context you allowed. The log says:

> prompt is too long (422090 tokens, max 24572)

That’s because we generated 12,000 lines of filler; each line is ~30–35 tokens → ~360k–420k tokens total. Let’s right-size it.

Make a right-sized test file

For ~24.6k ctx, ~800–850 lines is about right. For 32k ctx, ~1,050–1,100 lines.

24k-ish context file (safe on your 12 GB card)

cd /home/darren/ougway_env/llama.cpp

cat &gt; big24k.txt &lt;&lt;&#x27;EOF&#x27;
You are Ougway. Read the (very long) context below, then answer the question at the end.
If the context is repetitive, ignore repetition. Be concise and cite which numbered chunks influenced your answer.

--- BEGIN CONTEXT ---
EOF

# ~800 lines ≈ ~24k tokens
seq 1 820 | awk &#x27;{printf(&quot;[Chunk %04d] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.\n&quot;, $1)}&#x27; &gt;&gt; big24k.txt

cat &gt;&gt; big24k.txt &lt;&lt;&#x27;EOF&#x27;
--- END CONTEXT ---
Question: In 5 bullets, summarize the key ideas about symmetry and boundary conditions from the context above. Then list 3 questions that remain unanswered.
EOF

Run it:

/home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
  -ngl 999 -c 24576 -b 16 -t $(nproc) -n 400 --jinja \
  -f big24k.txt

Want to try 32k anyway?

Either shrink to ~1,080 lines and free a bit of VRAM:

# make ~32k file
cat &gt; big32k_slim.txt &lt;&lt;&#x27;EOF&#x27;
You are Ougway. Read the (very long) context below, then answer the question at the end.
If the context is repetitive, ignore repetition. Be concise and cite which numbered chunks influenced your answer.

--- BEGIN CONTEXT ---
EOF

seq 1 1080 | awk &#x27;{printf(&quot;[Chunk %04d] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.\n&quot;, $1)}&#x27; &gt;&gt; big32k_slim.txt

cat &gt;&gt; big32k_slim.txt &lt;&lt;&#x27;EOF&#x27;
--- END CONTEXT ---
Question: In 5 bullets, summarize the key ideas about symmetry and boundary conditions from the context above. Then list 3 questions that remain unanswered.
EOF

Then either:
– keep all layers on GPU but drop ctx a touch (28k: -c 28672), or
– keep 32k and free VRAM by moving a few layers to CPU + smaller batch:

/home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
  -ngl 30 -c 32768 -b 8 -t $(nproc) -n 400 --jinja \
  -f big32k_slim.txt

(If you’d rather stay fully on-GPU, use the Q6_K quant for ~1 GiB relief and keep -ngl 999.)

If you want, I can also give you a tiny bash function that auto-generates a filler file for any target token count (so you can just say mkctx 24000 or mkctx 32000).

Darren2025-09-19T21:27:52-07:00

Oh, it says there's still too many tokens, but the memory looked good. It was only 10… 10 gig.darren@ougway:~/ougway_env/llama.cpp$ cd /home/darren/ougway_env/llama.cpp

cat > big24k.txt <<'EOF'
You are Ougway. Read the (very long) context below, then answer the question at the end.
If the context is repetitive, ignore repetition. Be concise and cite which numbered chunks influenced your answer.

— BEGIN CONTEXT —
EOF

~800 lines ≈ ~24k tokens

seq 1 820 | awk '{printf("[Chunk %04d] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.\n", $1)}' >> big24k.txt

cat >> big24k.txt <<'EOF'
— END CONTEXT —
Question: In 5 bullets, summarize the key ideas about symmetry and boundary conditions from the context above. Then list 3 questions that remain unanswered.
EOF
darren@ougway:~/ougway_env/llama.cpp$ /home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
-m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
-ngl 999 -c 24576 -b 16 -t $(nproc) -n 400 –jinja \
-f big24k.txt
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 6511 (4ca088b0) with cc (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060) (0000:01:00.0) – 11537 MiB free
llama_model_loader: loaded meta data with 28 key-value pairs and 399 tensors from /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: – kv 0: general.architecture str = qwen3
llama_model_loader: – kv 1: general.type str = model
llama_model_loader: – kv 2: general.name str = Qwen3 8B Instruct
llama_model_loader: – kv 3: general.finetune str = Instruct
llama_model_loader: – kv 4: general.basename str = Qwen3
llama_model_loader: – kv 5: general.size_label str = 8B
llama_model_loader: – kv 6: qwen3.block_count u32 = 36
llama_model_loader: – kv 7: qwen3.context_length u32 = 40960
llama_model_loader: – kv 8: qwen3.embedding_length u32 = 4096
llama_model_loader: – kv 9: qwen3.feed_forward_length u32 = 12288
llama_model_loader: – kv 10: qwen3.attention.head_count u32 = 32
llama_model_loader: – kv 11: qwen3.attention.head_count_kv u32 = 8
llama_model_loader: – kv 12: qwen3.rope.freq_base f32 = 1000000.000000
llama_model_loader: – kv 13: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: – kv 14: qwen3.attention.key_length u32 = 128
llama_model_loader: – kv 15: qwen3.attention.value_length u32 = 128
llama_model_loader: – kv 16: tokenizer.ggml.model str = gpt2
llama_model_loader: – kv 17: tokenizer.ggml.pre str = qwen2
llama_model_loader: – kv 18: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", …
llama_model_loader: – kv 19: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
llama_model_loader: – kv 20: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",…
llama_model_loader: – kv 21: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: – kv 22: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: – kv 23: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: – kv 24: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: – kv 25: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>…
llama_model_loader: – kv 26: general.quantization_version u32 = 2
llama_model_loader: – kv 27: general.file_type u32 = 7
llama_model_loader: – type f32: 145 tensors
llama_model_loader: – type q8_0: 254 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 8.11 GiB (8.50 BPW)
load: printing all EOG tokens:
load: – 151643 ('<|endoftext|>')
load: – 151645 ('<|im_end|>')
load: – 151662 ('<|fim_pad|>')
load: – 151663 ('<|repo_name|>')
load: – 151664 ('<|file_sep|>')
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3
print_info: vocab_only = 0
print_info: n_ctx_train = 40960
print_info: n_embd = 4096
print_info: n_layer = 36
print_info: n_head = 32
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 4
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 12288
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 40960
print_info: rope_finetuned = unknown
print_info: model type = 8B
print_info: model params = 8.19 B
print_info: general.name = Qwen3 8B Instruct
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while… (mmap = true)
load_tensors: offloading 36 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 37/37 layers to GPU
load_tensors: CUDA0 model buffer size = 7669.77 MiB
load_tensors: CPU_Mapped model buffer size = 630.59 MiB
……………………………………………………………………………
llama_context: constructing llama_context
llama_context: n_batch is less than GGML_KQ_MASK_PAD – increasing to 64
llama_context: n_seq_max = 1
llama_context: n_ctx = 24576
llama_context: n_ctx_per_seq = 24576
llama_context: n_batch = 64
llama_context: n_ubatch = 64
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_per_seq (24576) < n_ctx_train (40960) — the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CUDA0 KV buffer size = 3456.00 MiB
llama_kv_cache: size = 3456.00 MiB ( 24576 cells, 36 layers, 1/1 seqs), K (f16): 1728.00 MiB, V (f16): 1728.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context: CUDA0 compute buffer size = 38.09 MiB
llama_context: CUDA_Host compute buffer size = 7.00 MiB
llama_context: graph nodes = 1267
llama_context: graph splits = 2
common_init_from_params: added <|endoftext|> logit bias = -inf
common_init_from_params: added <|im_end|> logit bias = -inf
common_init_from_params: added <|fim_pad|> logit bias = -inf
common_init_from_params: added <|repo_name|> logit bias = -inf
common_init_from_params: added <|file_sep|> logit bias = -inf
common_init_from_params: setting dry_penalty_last_n to ctx_size = 24576
common_init_from_params: warming up the model with an empty run – please wait … (–no-warmup to disable)
main: llama threadpool init, n_threads = 8
main: chat template is available, enabling conversation mode (disable it with -no-cnv)
*** User-specified prompt will pre-start conversation, did you mean to set –system-prompt (-sys) instead?
main: chat template example:
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant

system_info: n_threads = 8 (n_threads_batch = 8) / 8 | CUDA : ARCHS = 500,610,700,750,800,860,890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |

main: prompt is too long (27969 tokens, max 24572)
darren@ougway:~/ougway_env/llama.cpp$

ChatGPT2025-09-19T21:27:53-07:00 · gpt-5-thinking

You’re right on the memory; the OOM is gone. The new error is just token count > ctx. Two easy fixes:

Option 1 — shrink the prompt a bit

Your 24,576-token ctx showed 27969 tokens in the prompt. Drop the filler to ~700 lines and it’ll fit.

# remake the 24k file ~700 lines
cat &gt; big24k.txt &lt;&lt;&#x27;EOF&#x27;
You are Ougway. Read the (very long) context below, then answer the question at the end.
If the context is repetitive, ignore repetition. Be concise and cite which numbered chunks influenced your answer.

--- BEGIN CONTEXT ---
EOF

seq 1 700 | awk &#x27;{printf(&quot;[Chunk %04d] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.\n&quot;, $1)}&#x27; &gt;&gt; big24k.txt

cat &gt;&gt; big24k.txt &lt;&lt;&#x27;EOF&#x27;
--- END CONTEXT ---
Question: In 5 bullets, summarize the key ideas about symmetry and boundary conditions from the context above. Then list 3 questions that remain unanswered.
EOF

Run:

/home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
  -ngl 999 -c 24576 -b 16 -t $(nproc) -n 400 --jinja \
  -f big24k.txt

Option 2 — keep the longer prompt, bump ctx (still fits VRAM)

Go to 28,672 tokens (you’ve got headroom at ~10 GB VRAM):

/home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
  -ngl 999 -c 28672 -b 16 -t $(nproc) -n 400 --jinja \
  -f big24k.txt

Bonus: remove chat-template overhead

With --jinja, llama.cpp injects a chat template that adds tokens before your file. You can free some budget by disabling it:

/home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
  -ngl 999 -c 24576 -b 16 -t $(nproc) -n 400 \
  -no-cnv -f big24k.txt

(-no-cnv tells it to treat your file as raw text, no system/user wrappers.)

Pick any one of those and it should run clean. If you want to push 32k later, either:
– move a few layers to CPU (-ngl 30 -b 8), or
– switch to Q6_K quant to free ~1 GiB VRAM and keep -ngl 999.

Darren2025-09-19T21:30:26-07:00

Well, it's still scrolling through filler lines. Oop, it just stopped. Memory topped out at 11775. Absolutely perfect.d boundary conditions. This line is filler to test memory and long context.
[Chunk 0686] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0687] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0688] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0689] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0690] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0691] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0692] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0693] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0694] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0695] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0696] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0697] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0698] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0699] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
[Chunk 0700] The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context.
— END CONTEXT —
Question: In 5 bullets, summarize the key ideas about symmetry and boundary conditions from the context above. Then list 3 questions that remain unanswered.
assistant
<think>
Okay, let's tackle this. The user wants me to summarize the key ideas about symmetry and boundary conditions from the given context in 5 bullets, then list 3 unanswered questions.

First, I need to read through the context. All the chunks are the same: "The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This line is filler to test memory and long context." So every chunk repeats the same sentence. Since they're all the same, the key ideas are just those three topics mentioned.

For the summary bullets, I should mention symmetry, frequency domains, boundary conditions, and note that the context is repetitive. But the user said to ignore repetition, so maybe just focus on the main points. Wait, the context is all about symmetry, frequency domains, and boundary conditions, but each chunk is identical. So the key ideas are those three topics, and the repetition is just filler.

So the bullets could be:
1. Symmetry is a key topic discussed.
2. Frequency domains are covered.
3. Boundary conditions are part of the discussion.
4. The context is repetitive and serves as filler.
5. The content is primarily about the Flower Lattice primer's content.

But wait, the user wants 5 bullets. Let me check again. The main points are symmetry, frequency domains, boundary conditions, repetition, and the filler purpose. That makes five points.

Now for the unanswered questions. The context doesn't provide specific details beyond stating that these topics are discussed. So possible questions could be about how symmetry affects the Flower Lattice, specific applications of frequency domains, or examples of boundary conditions. Since the context doesn't go into depth, these would be unanswered.

Wait, the user said to list 3 questions that remain unanswered. Since the context is repetitive and only mentions the topics without explanations, the unanswered questions would be about the specifics of each topic. For example, how symmetry is applied, the exact role
>

ChatGPT2025-09-19T21:30:27-07:00 · gpt-5

Here’s what the model should reasonably output given that context:

Key ideas (5 bullets)
– The Flower Lattice primer emphasizes symmetry as a central feature.
– It highlights the role of frequency domains in describing or structuring the lattice.
– Boundary conditions are treated as essential constraints in the system.
– The primer frames these three concepts as tightly interrelated.
– Much of the context is repetitive filler, reinforcing the same three themes.

Unanswered questions (3 examples)
1. How exactly is symmetry defined or measured within the Flower Lattice framework?
2. What is the mathematical or physical role of frequency domains in this context?
3. Which boundary conditions are applied, and how do they alter lattice behavior?

Want me to show you how to tweak your prompt so that Ougway’s answer comes out in this clean bulleted style automatically?

Darren2025-09-19T21:32:46-07:00

No, I don't think that's necessary. I think it still got answered, just not maybe in the form that we were hoping for. I'm not worried about that. I'm more interested in, are these mechanics solid? Can we consistently run this model at that context level and not go out of memory? It looks like a yes, but it's really tight. 11757 out of 12288. Looks good to me, but are there any other conditions that could pop it out?

ChatGPT2025-09-19T21:32:48-07:00 · gpt-5-thinking

You nailed it: mechanics look solid. Hitting ~11.76 GB / 12.29 GB at 24.6k ctx means you’re right at the edge but stable. Here’s when it could still OOM and how to bullet-proof it:

What can push it over

  • Bigger ctx or more sequences: KV cache scales ~linearly with -c, and multiplies per concurrent sequence. (llama-server with --parallel N will allocate N×KV.)
  • Higher batch / ubatch: -b (and the internal ubatch) grows compute buffers a bit. Cranking -b can tip you over.
  • Extra features:
  • LoRA adapters ( -lora ) add VRAM.
  • Vision/multi-modal mmproj adds VRAM (if you ever load it).
  • --logits-all allocates a large logits buffer—avoid unless you truly need it.
  • Other GPU users: desktop compositor, stray CUDA jobs, browsers with heavy WebGPU/WebGL can shave 200–400 MiB at random.
  • 32k ctx on Q8_0 all-GPU: as you saw, KV wants ~4.6 GiB at 32k → OOM unless you free VRAM elsewhere.

Make it consistently safe

  • Daily driver setting: keep -c 24576 (or -c 28672 if you see headroom). That left you at ~11.76 GB → ~0.5 GB buffer.
  • Cap concurrency: for llama-server, set --parallel 1 (or 2 only if you drop ctx), so you don’t allocate multiple KV caches.
  • Moderate batch: stick to -b 16. If you hit the ceiling later, drop to -b 8 before touching ctx.
  • Avoid logits-all: don’t pass --logits-all unless debugging.
  • If you need 32k: either
  • move a few layers to CPU (-ngl 30, -b 8), or
  • switch to Q6_K (≈ −1 GiB model buffer) and keep -ngl 999.
  • LoRA planning: count on ~100–300 MiB extra VRAM depending on adapter size. If you add one, reduce ctx a notch (e.g., 24.6k → 22k) or batch.
  • Keep the GPU clean: close heavy browser tabs during tests; consider nvidia-smi --persistence-mode=1 so memory behavior is steadier across runs.

Quick “stability profile” you can run anytime

  • Live monitor: watch -n1 nvidia-smi
  • Log to CSV:
    nvidia-smi --query-gpu=timestamp,memory.used,memory.total,utilization.gpu,temperature.gpu \
    --format=csv -l 1 &gt;&gt; ~/gpu_log.csv
  • Smoke test command (safe headroom):
    /home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
    -m /home/darren/ougway_env/llama.cpp/models/qwen3/Qwen3-8B-Q8_0.gguf \
    -ngl 999 -c 24576 -b 16 -t $(nproc) -n 400 -no-cnv \
    -p &quot;Summarize in 5 bullets why Ougway prefers usage-aware retrieval.&quot;

Bottom line: for single-user, single-sequence, you’re good at 24.6k (and likely 28.6k). The only real “gotcha” is accidental concurrency or flipping on a memory-hungry flag. Keep those in check and Ougway will run smooth.

Darren2025-09-19T21:34:22-07:00

Well, I have the old 1060 card sitting here. Is it possible to actually utilize the VRAM on board that through a 2x PCIe connector?

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