MacBook Pro M4 Max 48GBOpción económica
48 GB Unified (+16)546 GB/s (+34)
C
Sube la velocidad estimada de decodificación alrededor de un 31%.87.8 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 31%.
~$2,499 MSRP
Can DeepSeek LLM 7B run on Radeon Pro W6800 32GB?
YES — Runs Great
C50Usable
Estimated from fit model
DeepSeek LLM 7B needs ~15.7 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~67 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 15.7 GB, 67.1 tok/s, Runs well
15.7 GB required32.0 GB available
49% VRAM used
Fit status
Runs well
Decode
67.1 tok/s
TTFT
2883 ms
Safe context
4K
Memory
15.7 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.3 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsDeepSeek LLM 7B on Radeon Pro W6800 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 67.1 tok/s decode · 2.9s TTFT (warm) · 168 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 | 67.1 tok/s | 1573 ms | 4K |
| Coding | C | Runs well | 67.1 tok/s | 2883 ms | 4K |
| Agentic Coding | C | Runs well | 67.1 tok/s | 4194 ms | 4K |
| Reasoning | C | Runs well | 67.1 tok/s | 3407 ms | 4K |
| RAG | C | Runs well | 67.1 tok/s | 5242 ms | 4K |
Inference speed
DeepSeek LLM 7B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for DeepSeek LLM 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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 | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Tight |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 51.5 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 48.0 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 45.3 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 30.2 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 11.4 | Too big |
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 LLM 7B (7B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C41 |
Q3_K_S | 3 | 3.4 GB | Low | C42 |
NVFP4 | 4 | 3.9 GB | Medium | C42 |
Q4_K_M | 4 | 4.3 GB | Medium | C42 |
Q5_K_M | 5 | 5.0 GB | High | C42 |
Q6_K | 6 | 5.7 GB | High | C42 |
Q8_0 | 8 | 7.5 GB | Very High | C43 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C46 |
Get started
Copy-paste commands to run DeepSeek LLM 7B on your machine.
Run
ollama run deepseek-llmOpciones de mejora
Hardware que ejecuta bien DeepSeek LLM 7B
Frequently asked questions
Can Radeon Pro W6800 32GB run DeepSeek LLM 7B?
Yes, Radeon Pro W6800 32GB can run DeepSeek LLM 7B with a C grade (Runs well). Expected decode speed: 67.1 tok/s.
How much VRAM does DeepSeek LLM 7B need?
DeepSeek LLM 7B (7B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.
What is the best quantization for DeepSeek LLM 7B?
The recommended quantization for DeepSeek LLM 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will DeepSeek LLM 7B run at on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, DeepSeek LLM 7B achieves approximately 67.1 tokens per second decode speed with a time-to-first-token of 2883ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run DeepSeek LLM 7B for coding?
For coding workloads, DeepSeek LLM 7B on Radeon Pro W6800 32GB receives a C grade with 67.1 tok/s and 4K context.
What context window can DeepSeek LLM 7B use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, DeepSeek LLM 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-llm-7b-on-radeon-pro-w6800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: