Cerebras-GPT 13B needs ~25.1 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q5_K_M quantization, expect ~123 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
123.0 tok/s
TTFT
1574 ms
Safe context
53K
Memory
25.1 GB / 48.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 123.0 tok/s | 858 ms | 53K |
| Coding | B | Runs well | 123.0 tok/s | 1574 ms | 53K |
| Agentic Coding | A | Runs well | 123.0 tok/s | 2289 ms | 53K |
| Reasoning | B | Runs well | 123.0 tok/s | 1860 ms | 53K |
| RAG | A | Runs well | 123.0 tok/s | 2861 ms | 53K |
Inference speed
Estimated decode speed (tokens/sec) for Cerebras-GPT 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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 | Q5_K_M | 130.8 | Fits |
| RTX 4090 24GB | 24 GB | Q5_K_M | 83.5 | Tight |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 75.3 | Tight |
| RTX 3090 24GB | 24 GB | Q5_K_M | 71.4 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 60.7 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 50.6 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 47.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 26.2 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 25.7 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 24.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 20.2 | Fits |
| RTX 4070 12GB | 12 GB | Q5_K_M | 9.0 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 5.7 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 3.2 | Too big |
Estimates for single-stream decoding at Q5_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 Cerebras-GPT 13B (13B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B58 |
Q3_K_S | 3 | 6.4 GB | Low | B58 |
NVFP4 | 4 | 7.3 GB | Medium | B58 |
Q4_K_M | 4 | 7.9 GB | Medium | B58 |
Q5_K_M | 5 | 9.4 GB | High | B59 |
Q6_K | 6 | 10.7 GB | High | B59 |
Q8_0 | 8 | 13.9 GB | Very High | B60 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B64 |
Copy-paste commands to run Cerebras-GPT 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "cerebras/Cerebras-GPT-13B" \
--hf-file "Cerebras-GPT-13B-Q5_K_M.gguf" \
-c 4096 -ngl 99Yes, RTX PRO 5000 Blackwell 48GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 123.0 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 25.1 GB of memory with Q5_K_M quantization.
The recommended quantization for Cerebras-GPT 13B is Q5_K_M, which balances quality and memory efficiency.
On RTX PRO 5000 Blackwell 48GB, Cerebras-GPT 13B achieves approximately 123.0 tokens per second decode speed with a time-to-first-token of 1574ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on RTX PRO 5000 Blackwell 48GB receives a B grade with 123.0 tok/s and 53K context.
On RTX PRO 5000 Blackwell 48GB, Cerebras-GPT 13B can safely use up to 53K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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Preview: