Giancarmine Salucci
4ad296608b
- 5 models: SmolLM3-3B, Gemma4-E2B/E4B, Qwen3-4B, Qwen3.5-9B - TurboQuant image (FORCE_MMQ): +6-11% free speed on Turing GPUs - Bigctx profiles (-nkvo KV in RAM): 2-16x context gain - turbo2 KV: 2x smaller, benchmarked against PPL quality gate - Per-model env files with justified parameters - kv_quant_test.sh + cpu_ctx_test.sh benchmark scripts - docs/FINDINGS.md: surprises, pitfalls, recommendations - docs/ARCHITECTURE.md: compose + test script design
27 lines
830 B
Plaintext
27 lines
830 B
Plaintext
# ==============================================================================
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# Gemma 4 E4B-it Q4_K_M — bigctx variant (KV in RAM via -nkvo)
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# Benchmarked 2026-05-06 v4 (TurboQuant FORCE_MMQ): turbo2 rec ctx=163840
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# +139264 tokens vs pure-GPU 24576. turbo2 KV = 2.1 KB/tok vs q4_0 4.5 KB/tok.
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# Speed at ctx=163840: baseline 30.0 t/s, est. 17.8@50% / 22.4@25% (PCIe BW).
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# RAM at 163840: 346 MiB KV. ngl=42 (all layers on GPU).
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# Use this profile when you need >24K context; otherwise use gemma4-e4b.
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# ==============================================================================
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MODEL_FILE=google_gemma-4-E4B-it-Q4_K_M.gguf
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N_GPU_LAYERS=42
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CTX_SIZE=163840
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THREADS=6
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THREADS_BATCH=6
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BATCH_SIZE=512
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UBATCH_SIZE=128
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CACHE_TYPE_K=turbo2
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CACHE_TYPE_V=turbo2
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PARALLEL=1
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EXTRA_ARGS=--flash-attn on --mmap --no-kv-offload
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