Mac Studio M3 Ultra 96GBOpção econômica
96 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.112 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Can DeepSeek R1 Distill Llama 8B run on Radeon Pro W7900 48GB?
YES — Runs Great
C47Usable
Estimated from fit model
DeepSeek R1 Distill Llama 8B needs ~11.5 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~105 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 11.5 GB, 104.5 tok/s, Runs well
11.5 GB required48.0 GB available
24% VRAM used
Fit status
Runs well
Decode
104.5 tok/s
TTFT
1853 ms
Safe context
639K
Memory
11.5 GB / 48.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsDeepSeek R1 Distill Llama 8B on Radeon Pro W7900 48GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 104.5 tok/s decode · 1.9s TTFT (warm) · 261 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 104.5 tok/s | 1011 ms | 639K |
| Coding | C | Runs well | 104.5 tok/s | 1853 ms | 639K |
| Agentic Coding | C | Runs well | 104.5 tok/s | 2696 ms | 639K |
| Reasoning | C | Runs well | 104.5 tok/s | 2190 ms | 639K |
| RAG | C | Runs well | 104.5 tok/s | 3370 ms | 639K |
Inference speed
DeepSeek R1 Distill Llama 8B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for DeepSeek R1 Distill Llama 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 | 95.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 90.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 77.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 49.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 48.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 45.1 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 40.7 | Offloads |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 39.6 | Fits |
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.
Quantization options
How DeepSeek R1 Distill Llama 8B (8B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C42 |
Q3_K_S | 3 | 3.9 GB | Low | C42 |
NVFP4 | 4 | 4.5 GB | Medium | C42 |
Q4_K_M | 4 | 4.9 GB | Medium | C42 |
Q5_K_M | 5 | 5.8 GB | High | C42 |
Q6_K | 6 | 6.6 GB | High | C42 |
Q8_0 | 8 | 8.6 GB | Very High | C43 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C45 |
Get started
Copy-paste commands to run DeepSeek R1 Distill Llama 8B on your machine.
Run
lms load hf-unsloth--deepseek-r1-distill-llama-8b-gguf && lms server startOpções de upgrade
Hardware que roda bem DeepSeek R1 Distill Llama 8B
Frequently asked questions
Can Radeon Pro W7900 48GB run DeepSeek R1 Distill Llama 8B?
Yes, Radeon Pro W7900 48GB can run DeepSeek R1 Distill Llama 8B with a C grade (Runs well). Expected decode speed: 104.5 tok/s.
How much VRAM does DeepSeek R1 Distill Llama 8B need?
DeepSeek R1 Distill Llama 8B (8B parameters) requires approximately 11.5 GB of memory with Q4_K_M quantization.
What is the best quantization for DeepSeek R1 Distill Llama 8B?
The recommended quantization for DeepSeek R1 Distill Llama 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will DeepSeek R1 Distill Llama 8B run at on Radeon Pro W7900 48GB?
On Radeon Pro W7900 48GB, DeepSeek R1 Distill Llama 8B achieves approximately 104.5 tokens per second decode speed with a time-to-first-token of 1853ms using Q4_K_M quantization.
Can Radeon Pro W7900 48GB run DeepSeek R1 Distill Llama 8B for coding?
For coding workloads, DeepSeek R1 Distill Llama 8B on Radeon Pro W7900 48GB receives a C grade with 104.5 tok/s and 639K context.
What context window can DeepSeek R1 Distill Llama 8B use on Radeon Pro W7900 48GB?
On Radeon Pro W7900 48GB, DeepSeek R1 Distill Llama 8B can safely use up to 639K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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