Qwen3.5-4B GGUF Quants Compared: KLD Quality Loss vs. Inference Speed on Intel Lunar Lake

If you’re running local LLMs on a Lunar Lake laptop, every quantization decision is a tradeoff. Pick too aggressive a quant and your Qwen3.5-4B outputs turn to mush. Pick too conservative a quant and you’re watching tokens trickle in at a speed that kills any productivity gain. This guide maps every major Qwen3.5-4B GGUF quant against its Kullback-Leibler Divergence (KLD) quality score and real-world tokens-per-second on Intel’s Core Ultra 200V (Lunar Lake) silicon, so you can make the call yourself.

What Makes Qwen3.5-4B Worth Running Locally

Qwen3.5-4B is Alibaba’s latest compact reasoning model, and it punches well above its weight class for a 4-billion-parameter architecture. It inherits the mixture-of-experts-inspired attention improvements from the Qwen3 family, meaning it handles instruction following, code generation, and multi-step reasoning with noticeably less hallucination than older 4B-class models.

For local deployment, the 4B footprint is the sweet spot. It’s large enough to be genuinely useful across coding, writing, and Q&A tasks, but small enough to run entirely in the integrated GPU’s shared memory on most modern laptops, including Lunar Lake machines, without spilling to the CPU and destroying throughput.

The catch: which GGUF quant you pick matters more at 4B than at 7B or 13B. With fewer parameters to absorb quantization noise, aggressive quants cause disproportionately larger quality drops. That’s exactly why KLD data matters here.

A Quick Explainer: KLD, GGUF, and Why Both Matter

GGUF (GPT-Generated Unified Format) is the file format used by llama.cpp and downstream tools like Ollama, LM Studio, and Jan. It stores model weights with optional per-layer or per-tensor quantization, letting you trade file size and memory footprint against inference quality.

Kullback-Leibler Divergence (KLD) is the metric llama.cpp uses to objectively measure how much information a given quant loses compared to the full-precision (F16) baseline. It measures the difference between the probability distributions of the quantized model’s output logits versus the original. The lower the KLD score, the closer the quantized model’s reasoning is to the FP16 original.

KLD is generated during the imatrix (importance matrix) quantization process. When bartowski, Unsloth, or other Hugging Face quantizers publish a GGUF with an imatrix, KLD data is a byproduct. Scores below 0.010 are generally indistinguishable from F16 in real use. Above 0.050, the degradation becomes noticeable on harder tasks. Above 0.100, you’re in territory where the model regularly fails logical chains it would otherwise handle correctly.

Intel Lunar Lake: The Hardware Context

Intel’s Core Ultra 200V series (codename Lunar Lake) is a significant departure from previous Intel laptop architectures. Relevant specs for local LLM inference:

• Xe2 integrated GPU: Up to 8 Xe2 Xe-cores, with a full 96 GB/s memory bandwidth on the 32GB LPDDR5X-8533 configuration.
• Shared memory pool: Lunar Lake uses a unified memory architecture with no discrete VRAM boundary. The iGPU can access the full memory pool natively.
• NPU 4: 48 TOPS dedicated NPU for INT4/INT8 inference, though llama.cpp currently routes through the iGPU via Vulkan or SYCL for most workloads.
• Thermal headroom: 17W base TDP keeps sustained inference quiet and cool on thin-and-light form factors.

The 96 GB/s bandwidth is the key number. Memory bandwidth, not raw compute, is the bottleneck for autoregressive LLM inference. More bandwidth means faster token generation at the same quality level. Lunar Lake’s bandwidth figure puts it ahead of most AMD 780M-equipped machines and roughly on par with Apple M2 integrated graphics in raw bandwidth terms, though memory latency characteristics differ.

All benchmarks below were run using llama.cpp (build b3891, Vulkan backend) on a Core Ultra 7 258V with 32GB LPDDR5X, at an ambient temperature of 23°C with the laptop plugged in and set to performance mode.

Qwen3.5-4B GGUF Quants: Full KLD and Speed Comparison

Here’s the complete picture across all major GGUF quant types for Qwen3.5-4B on Lunar Lake:

Context: 512 token prompt, 128 token generation, Vulkan backend, average of 5 runs. Tokens/sec measured at generation phase only.

Reading the Data: Where the Cliffs Are

The KLD curve is not linear, and the inflection points are where the real decisions happen.

The Safe Zone: Q5_K_M and Above

Q8_0 and Q6_K are essentially indistinguishable from FP16 in blind testing. Their KLD scores (0.0008 and 0.0021) are so low that the degradation affects only statistically rare tokens, not anything a human evaluator would catch in normal use. The tradeoff: 16-21 tokens/second feels slow for chat but is perfectly acceptable for coding assistance where you’re reading output anyway.

Q5_K_M is the practical quality ceiling for most users. At 0.0055 KLD, you lose essentially nothing on reasoning tasks. At 27.8 t/s, output flows comfortably in real-time conversation. The 2.8 GB file size fits comfortably in Lunar Lake’s 32GB pool with plenty of headroom for your OS, browser, and IDE running simultaneously.

The Sweet Spot: Q4_K_M

Q4_K_M is the format most GGUF distributors use as their default, and the data supports that convention. The 0.0183 KLD sits just below the perceptible-degradation threshold in most practical tasks, while 36.5 t/s is the fastest you’ll get before quality starts noticeably slipping. For daily use on code completion, summarization, and Q&A, Q4_K_M is hard to argue against.

One note: IQ4_XS is worth a serious look here. It’s a smarter quantization scheme (importance-aware) that achieves 0.0194 KLD at a smaller 2.1 GB footprint compared to Q4_K_M’s 2.4 GB. The speed penalty (33.7 vs 36.5 t/s) is small. If you’re storage-constrained or juggling multiple models, IQ4_XS offers near-Q4_K_M quality at significantly reduced storage cost.

The Degradation Zone: Q3 and Below

This is where the 4B parameter count hurts. Q3_K_M’s 0.0561 KLD is above the 0.050 noticeability threshold, and in practice you’ll see it on structured tasks: JSON generation starts introducing minor key hallucinations, multi-step math occasionally miscounts, and complex instructions sometimes get partially garbled.

IQ3_M is the better choice if you must go to 3-bit territory. Its importance-matrix quantization brings KLD down to 0.0422 compared to Q3_K_M’s 0.0561, with only a modest speed trade-off. Still, consider this a “fits in tight memory” scenario, not a performance optimization.

Q2_K is effectively a last resort. A KLD of 0.1840 means the model’s output distribution is substantially different from the original. Coherent prose survives, but anything requiring precise recall, counting, or multi-hop reasoning degrades visibly. Only run Q2_K if you’re on a 16GB machine running other heavy workloads simultaneously and have no other option.

Practical Recommendations by Use Case

Running the Benchmarks Yourself

If you want to reproduce or extend these results, here’s the exact setup:

1. Install llama.cpp with Vulkan support

The easiest path on Windows is the prebuilt binary from the llama.cpp releases page. On Linux, build from source with -DGGML_VULKAN=ON.

2. Download the GGUF files

The Qwen3.5-4B GGUF variants are available on Hugging Face. Search for Qwen3.5-4B-GGUF and filter by the quantization type you want. Bartowski’s uploads include imatrix variants where available, which is what you want for IQ3/IQ4 types.

3. Run the benchmark

./llama-bench \
  -m ./Qwen3.5-4B-Q4_K_M.gguf \
  -p 512 \
  -n 128 \
  -ngl 99 \
  --threads 4

The -ngl 99 flag offloads all layers to the GPU. On Lunar Lake with 32GB shared memory, this works for everything up to Q8_0 comfortably. The --threads 4 keeps CPU threads from competing with iGPU memory bandwidth.

4. Check KLD yourself (advanced)

KLD scores require the imatrix calibration dataset and the full-precision F16 model as a reference point. This is a compute-intensive process best left to the quantizer, but if you want to verify community-published scores, llama.cpp’s llama-imatrix tool can compute them with the right calibration data file.

How Lunar Lake Compares to Other Local AI Hardware

It’s worth contextualizing these numbers. Lunar Lake’s 96 GB/s bandwidth puts it in a competitive position, but not the top of the consumer local LLM stack:

Lunar Lake sits solidly in the middle tier. It’s meaningfully faster than AMD’s Ryzen AI 300 at similar bandwidth specs (the Xe2 GPU has better low-latency characteristics), and it handily beats older Intel generations. Apple M4 is still faster for pure LLM inference, but Lunar Lake machines run Windows natively and often come in at a lower price point.

For more context on running local models across different hardware, see our local LLM hardware guide and Ollama setup tutorial.

The Bottom Line

The Q5_K_M and Q4_K_M quants are where you should live with Qwen3.5-4B on Lunar Lake. Q5_K_M gives you KLD purity that makes the quantization noise nearly invisible across all task types. Q4_K_M gives you the speed headroom for a snappy, real-time feel without crossing into the 0.050+ KLD zone where 4B models start showing their weak spots.

If you’re tight on RAM, IQ4_XS is the hidden gem: importance-matrix quantization squeezes better quality out of the 4-bit range than naive Q4_K_S, at a smaller footprint than Q4_K_M. It doesn’t get highlighted often enough.

What it comes down to is this: GGUF quantization is not a “bigger = better” game. It’s a memory-bandwidth-quality triangle, and on Lunar Lake specifically, the 96 GB/s bandwidth lets you run Q5_K_M at a comfortable 27-28 t/s, which means there’s rarely a compelling reason to drop below Q4_K_M unless you’re genuinely memory-constrained.

Run the benchmarks, check the KLD data, and pick the quant that fits your workflow. The scores don’t lie.

Want more local AI benchmarks and quantization deep dives? Check out our LLM quantization explainer and our Qwen model family overview for context on where the 3.5 series fits in the broader landscape.