DeepSeek R1 Distill 8B needs ~21.6 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~102 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
102.2 tok/s
TTFT
1894 ms
Safe context
33K
Memory
21.6 GB / 92.2 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 | B | Runs well | 102.2 tok/s | 1033 ms | 33K |
| Coding | B | Runs well | 102.2 tok/s | 1894 ms | 33K |
| Agentic Coding | B | Runs well | 102.2 tok/s | 2755 ms | 33K |
| Reasoning | B | Runs well | 102.2 tok/s | 2238 ms | 33K |
| RAG | B | Runs well | 102.2 tok/s | 3444 ms | 33K |
Inference speed
Estimated decode speed (tokens/sec) for DeepSeek R1 Distill 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 112.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 112.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 102.2 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 96.9 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 83.3 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 82.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 82.6 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 52.9 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 52.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 48.5 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 42.6 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 26.6 | Heavy offload |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How DeepSeek R1 Distill 8B (8B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B56 |
Q3_K_S | 3 | 3.9 GB | Low | B56 |
NVFP4 | 4 | 4.5 GB | Medium | B56 |
Q4_K_M | 4 | 4.9 GB | Medium | B56 |
Q5_K_M | 5 | 5.8 GB | High | B56 |
Q6_K | 6 | 6.6 GB | High | B56 |
Q8_0 | 8 | 8.6 GB | Very High | B56 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | B57 |
Copy-paste commands to run DeepSeek R1 Distill 8B on your machine.
Run
ollama run deepseek-r1:8bYes, Mac Studio M2 Ultra 128GB can run DeepSeek R1 Distill 8B with a B grade (Runs well). Expected decode speed: 102.2 tok/s.
DeepSeek R1 Distill 8B (8B parameters) requires approximately 21.6 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill 8B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M2 Ultra 128GB, DeepSeek R1 Distill 8B achieves approximately 102.2 tokens per second decode speed with a time-to-first-token of 1894ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 8B on Mac Studio M2 Ultra 128GB receives a B grade with 102.2 tok/s and 33K context.
On Mac Studio M2 Ultra 128GB, DeepSeek R1 Distill 8B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Not always. Mac Studio M2 Ultra 128GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-8b-on-m2-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: