Disclosure: StrideNote Studio is a research and media production studio that evaluates local AI tools as part of its workflow.
A Mac with 24 GB of unified memory loads a 19 GB model into the same pool the GPU reads from. No copying. No VRAM ceiling. Just a single allocation that determines whether inference runs at 14 tokens per second or slows to a crawl. That is the promise and the constraint of Apple Silicon for local LLMs, and it is the frame for the most contested open-weight matchup of 2026: Google DeepMind’s Gemma 4 31B versus Alibaba’s Qwen 3.5 27B.
Both models are dense, both sit just under 30 billion parameters, and both fit on a single consumer-tier Mac with enough quantization. But they make radically different trade-offs on unified memory. Gemma 4 31B scores higher on reasoning benchmarks and ranks third among open models on the LMArena leaderboard at 1452 ELO. Qwen 3.5 27B leads on raw inference speed and multilingual coverage, and its thinking mode adds a capability Gemma 4 does not natively match. The choice between them is not a winner-take-all verdict. It depends on your memory tier, your context window needs, and how much thinking you actually want the model to do.
What does unified memory mean for 27B and 31B models?
Apple Silicon’s unified memory architecture is the reason a Mac can run models that would exceed the VRAM of many discrete GPUs. On an M-series chip, the 19 GB Gemma 4 31B Q4_K_M GGUF loads into the same memory pool the GPU uses. There is no PCIe transfer and no artificial VRAM boundary. The catch is that all of the model must fit alongside the operating system, the KV cache for your context window, and whatever else is running.
The math is straightforward. The model at Q4_K_M takes 17 to 20 GB depending on the format. A 32,768-token context window adds roughly 10 GB of KV cache for a 31B dense model with 48 layers, as documented by SudoAll’s Apple Silicon benchmarks. The total is 29 to 30 GB, which exceeds the 32 GB of available memory on a mid-tier Mac. The system swaps to SSD, and throughput drops from usable to frustrating. This is the core tradeoff behind our guide on how to pick a local model for your RAM.
The solution for each memory tier is different. On a 24 GB Mac, Gemma 4 31B barely fits with a reduced context window. On a 36 GB M4 Pro, it runs at 20 to 35 tok/s. On a 48 GB M4 Max with 546 GB/s bandwidth, it reaches 40 to 50 tok/s. The same principle applies to Qwen 3.5 27B, which takes roughly 17 GB at 4-bit and leaves more headroom for context, but the KV cache cost is lower because Qwen uses DeltaNet linear attention layers that compress the memory footprint.
How does Gemma 4 31B perform on benchmarks?
The case for Gemma 4 31B is the benchmark sheet. On AIME 2026, the model scores 89.2 percent. On LiveCodeBench v6, it scores 80.0 percent. On GPQA Diamond, it scores 84.3 percent. The jump from Gemma 3 27B is not incremental. The previous generation scored 20.8 percent on AIME and 29.1 percent on LiveCodeBench.
What matters more for local deployment is token efficiency. Benjamin Marie’s independent evaluation on The Kaitchup found that Gemma 4 31B produces significantly shorter reasoning traces than Qwen 3.5 27B. Qwen often generates 60,000 to 100,000 thinking tokens before answering. Gemma rarely exceeds 20,000. On a memory-bandwidth-bound Mac where every generated token costs time, that difference compounds across a session.
Gemma 4 31B also demonstrates unusually high consistency across runs. Google recommends a temperature of 1.0 and a top-k of 64, settings that normally increase output variability. The model maintains stable answers anyway. The practical result is that pass@1 and pass@k scores are close, which means the first response is more likely to be correct.
How does Qwen 3.5 27B perform on benchmarks?
Qwen 3.5 27B does not match Gemma 4 on the hardest math and coding benchmarks. The model scores roughly 49 percent on AIME 2025 against Gemma 4’s 89.2 percent on AIME 2026, and around 43 percent on LiveCodeBench v5 against Gemma 4’s 80.0 percent on v6. The gaps are large enough that the comparison on those tasks is not close.
Where Qwen 3.5 27B leads is on MMLU-Pro and GPQA Diamond. It achieves 86.1 percent on MMLU-Pro against Gemma 4’s 85.2 percent, and 85.5 percent on GPQA Diamond against Gemma 4’s 84.3 percent. The margins are small but consistent across multiple evaluation runs. Qwen also supports 201 languages against Gemma 4’s 140, and its 262K native context window is slightly larger than Gemma 4’s 256K.
The headline advantage for local deployment is inference speed. On an M4 Pro 24 GB at Q4_K_M, Qwen 3.5 27B generates 18 to 25 tok/s compared to Gemma 4 31B’s 14 tok/s. The speed gap comes from the smaller parameter count and the linear attention architecture, which reduces memory bandwidth pressure during decode.
Which model runs faster on Apple Silicon?
The speed question on unified memory is a bandwidth question. Every token generated requires reading the model weights from memory. A 31B dense model reads 31 billion parameters per token. A 27B dense model reads 27 billion. The difference at Q4 is about 2 GB less data per forward pass.
On a 24 GB M4 Pro, community benchmarks measured Gemma 4 31B at 14 tok/s with 1.4 seconds to first token using Ollama’s Q4_K_M. Qwen 3.5 27B at the same quantization runs at 18 to 25 tok/s. On a 48 GB M4 Max, the gap narrows because the higher bandwidth reduces the weight-read penalty. The M4 Max at 546 GB/s delivers roughly 35 to 45 tok/s for Gemma 4 31B and 45 to 55 tok/s for Qwen 3.5 27B.
The counterintuitive finding is that MoE models in both families outperform their dense siblings on unified memory. The Gemma 4 26B-A4B activates only 3.8 billion parameters per token but loads the full 26 billion into memory. At Q4, it generates 25 to 40 tok/s on a 24 GB Mac while scoring 1441 on LMArena. The Qwen 3.5 35B-A3B follows the same pattern. The memory cost is the full model size, but the decode speed matches a much smaller dense model.
What is the hidden memory cost of thinking mode?
Qwen 3.5’s configurable thinking mode is its most distinctive feature. It allows the model to produce chain-of-thought reasoning tokens before the final answer, which improves performance on complex tasks. The cost is that those reasoning tokens share the generation budget and consume KV cache memory.
Omar Shabab’s local benchmark on a Mac Studio M3 with 512 GB of unified memory documents the effect precisely. Qwen 3.5 27B at Q8_0 took an average of 65.6 seconds per prompt with thinking enabled. Gemma 4 31B at full bf16 completed the same prompts in 8.0 seconds. The thinking trace consumed the generation budget so aggressively that the first test run produced empty responses because Qwen burned all 512 tokens on reasoning.
The implication for unified memory is direct. Thinking mode multiplies the number of tokens generated per query. Each token requires reading the KV cache alongside the model weights. On a memory-bandwidth-bound Mac, the throughput drops in proportion to the thinking trace length. Gemma 4 31B achieves its speed advantage partly because it simply generates fewer tokens.
What is the verdict by memory tier?
For a 24 GB Mac, the safe choice is the Gemma 4 26B-A4B at 15 to 18 GB Q4, which delivers near-frontier scores at 25 to 40 tok/s. If you need the dense model specifically, Qwen 3.5 27B gives you more usable context headroom and faster tokens than Gemma 4 31B. The 31B dense model fits on 24 GB but requires a reduced context window of roughly 8,000 tokens to avoid swapping.
For a 36 GB to 48 GB Mac, Gemma 4 31B becomes viable. At Q4_K_M on a 36 GB M4 Pro, expect 20 to 35 tok/s with a comfortable context window. On a 48 GB M4 Max at the same quantization, expect 40 to 50 tok/s. The 48 GB Mac is the first tier where Gemma 4 31B outperforms Qwen 3.5 27B on both quality and speed, because the bandwidth is high enough that the larger model size does not bottleneck decode.
For a 64 GB and above Mac, the question shifts from whether to which precision. Gemma 4 31B at Q8 or even bf16 becomes possible. At bf16, the model occupies 58 GB and delivers reference-quality outputs at 40 to 50 tok/s on an M4 Max. Qwen 3.5 122B-A10B at 4-bit also fits, requiring roughly 75 GB. The 122B MoE activates only 10 billion parameters and scores higher than either 27B or 31B model, but it demands a machine with 128 GB or more to leave room for context and the operating system.
The software stack matters too. Ollama supports both models equally well on Apple Silicon using Metal acceleration. MLX runs both families natively, and its 4-bit format for Gemma 4 31B is 2 GB smaller than the GGUF Q4_K_M variant, which can be the difference between fitting in memory and swapping on a 32 GB machine. Community benchmarks show oMLX recovering 8.5x throughput on a 32 GB Mac by using this format difference and handling KV cache overflow with SSD-backed blocks instead of swapping.
Neither model is universally faster or universally smarter. Gemma 4 31B is the stronger model for reasoning, coding, and agentic workflows on Macs with 48 GB or more of unified memory. Qwen 3.5 27B is the better choice for 24 GB to 36 GB configurations where context headroom and faster decoding matter more than the top benchmark scores. The unified memory advantage is real. The right pick depends on where your Mac sits in the memory bandwidth stack. For the wider field beyond these two, see our roundup of which local LLM to run in 2026.