NVIDIA H20 96GBBudget pick4000 GB/s (+3454)
C
Raises estimated decode speed by about 440%.151.8 tok/s decode
Raises estimated decode speed by about 440%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP
Qwen3.5 35B A3B needs ~36.7 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~28 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
28.1 tok/s
TTFT
6882 ms
Safe context
142K
Memory
36.7 GB / 69.1 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 28.1 tok/s | 3754 ms | 142K |
| Coding | C | Runs well | 28.1 tok/s | 6882 ms | 142K |
| Agentic Coding | C | Runs well | 28.1 tok/s | 10010 ms | 142K |
| Reasoning | C | Runs well | 28.1 tok/s | 8133 ms | 142K |
| RAG | C | Runs well | 28.1 tok/s | 12513 ms | 142K |
How Qwen3.5 35B A3B (35B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | C42 |
Q3_K_S | 3 | 17.2 GB | Low | C43 |
NVFP4 | 4 |
Copy-paste commands to run Qwen3.5 35B A3B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "unsloth/Qwen3.5-35B-A3B-GGUF" \
--hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
NVIDIA H20 96GBBudget pickRaises estimated decode speed by about 440%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP
NVIDIA A100 80GBBest valueRaises estimated decode speed by about 185%.
~$15,000 MSRP
Yes, MacBook Pro M4 Max 96GB can run Qwen3.5 35B A3B with a C grade (Runs well). Expected decode speed: 28.1 tok/s.
Qwen3.5 35B A3B (35B parameters) requires approximately 36.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 96GB, Qwen3.5 35B A3B achieves approximately 28.1 tokens per second decode speed with a time-to-first-token of 6882ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 35B A3B on MacBook Pro M4 Max 96GB receives a C grade with 28.1 tok/s and 142K context.
On MacBook Pro M4 Max 96GB, Qwen3.5 35B A3B can safely use up to 142K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-35b-a3b-gguf-on-m4-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
19.6 GB |
| Medium |
| C43 |
Q4_K_M | 4 | 21.3 GB | Medium | C44 |
Q5_K_M | 5 | 25.2 GB | High | C44 |
Q6_K | 6 | 28.7 GB | High | C45 |
Q8_0Best for your GPU | 8 | 37.5 GB | Very High | C48 |
F16 | 16 | 71.8 GB | Maximum | F0 |
Not always. MacBook Pro M4 Max 96GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.