RTX PRO 5000 Blackwell 48GBBudget pick1344 GB/s (+544)
C
Raises estimated decode speed by about 157%.68.5 tok/s decode
Raises estimated decode speed by about 157%.
~$4,999 MSRP
Qwen3.5 27B needs ~27.4 GB VRAM. Mac Studio M1 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~27 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
26.7 tok/s
TTFT
7247 ms
Safe context
110K
Memory
27.4 GB / 46.1 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 | 26.7 tok/s | 3953 ms | 110K |
| Coding | C | Runs well | 26.7 tok/s | 7247 ms | 110K |
| Agentic Coding | C | Runs well | 26.7 tok/s | 10541 ms | 110K |
| Reasoning | C | Runs well | 26.7 tok/s | 8564 ms | 110K |
| RAG | C | Runs well | 26.7 tok/s | 13176 ms | 110K |
How Qwen3.5 27B (27B params) fits at each quantization level on Mac Studio M1 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C44 |
Q3_K_S | 3 | 13.2 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run Qwen3.5 27B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "unsloth/Qwen3.5-27B-GGUF" \
--hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
RTX PRO 5000 Blackwell 48GBBudget pickRaises estimated decode speed by about 157%.
~$4,999 MSRP
RTX 6000 Ada 48GBBest valueRaises estimated decode speed by about 79%.
~$6,800 MSRP
Yes, Mac Studio M1 Ultra 64GB can run Qwen3.5 27B with a C grade (Runs well). Expected decode speed: 26.7 tok/s.
Qwen3.5 27B (27B parameters) requires approximately 27.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 27B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M1 Ultra 64GB, Qwen3.5 27B achieves approximately 26.7 tokens per second decode speed with a time-to-first-token of 7247ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 27B on Mac Studio M1 Ultra 64GB receives a C grade with 26.7 tok/s and 110K context.
On Mac Studio M1 Ultra 64GB, Qwen3.5 27B can safely use up to 110K 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--qwen3-5-27b-gguf-on-m1-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
15.1 GB |
| Medium |
| C45 |
Q4_K_M | 4 | 16.5 GB | Medium | C46 |
Q5_K_M | 5 | 19.4 GB | High | C47 |
Q6_K | 6 | 22.1 GB | High | C48 |
Q8_0Best for your GPU | 8 | 28.9 GB | Very High | C48 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Not always. Mac Studio M1 Ultra 64GB 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.