ByteShape Quantization Boosts Qwen3.6-35B-A3B Generation Speed by 30% on 6GB Laptop
New ByteShape CPU-5 quant achieves 33.1 tok/s vs Unsloth's 25.4 tok/s.
A developer compared ByteShape's new quantization for the Qwen3.6-35B-A3B model against Unsloth's UD-IQ4_XS on a 2021 Asus ROG Zephyrus G14 laptop with an RTX 3060 (6GB VRAM), AMD Ryzen 7 5800HS, and 24GB RAM. Both models were partially offloaded to CPU via llama.cpp (commit 9203) with a context size of 65536, mlock enabled, and ubatch size 2048. The ByteShape CPU-5 quant (Q4_K_S-4.22bpw, 18.3GB) was benchmarked against Unsloth's UD-IQ4_XS (17.7GB). Using a 10k-token prompt followed by 1.5-2k token generation, ByteShape achieved 33.1 tok/s generation speed—30% faster than Unsloth's 25.4 tok/s. However, prompt processing was 4% slower (564 vs 585 tok/s).
The performance difference is partly attributed to quantization type: Unsloth uses IMatrix (IQ) optimization, which is slower on CPU, while ByteShape uses regular Q4_K_S quants. The developer notes that a fairer comparison would use ByteShape's GPU-5 quant (also IMatrix). Despite being slightly larger, ByteShape's quant offers a clear generation speedup, making it ideal for agentic coding tasks where generation latency matters. The trade-off in prompt processing speed is minor for most use cases. This demonstrates that quantization choice significantly impacts real-world inference performance on constrained hardware.
- ByteShape CPU-5 quant achieved 33.1 tok/s generation speed, 30% faster than Unsloth's UD-IQ4_XS (25.4 tok/s) on a 6GB VRAM laptop.
- ByteShape quant is slightly larger (18.3GB vs 17.7GB) but uses regular Q4_K_S quantization, which is faster on CPU than Unsloth's IMatrix method.
- Prompt processing was 4% slower for ByteShape (564 vs 585 tok/s), but the generation gain is critical for interactive agent tasks.
Why It Matters
Enables faster generation for large models on limited hardware, improving real-time agent performance.