Cerebras-GPT 13B needs ~33.4 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q5_K_M quantization, expect ~18 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
17.9 tok/s
TTFT
10846 ms
Safe context
131K
Memory
33.4 GB / 108.8 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 | 17.9 tok/s | 5916 ms | 131K |
| Coding | B | Runs well | 17.9 tok/s | 10846 ms | 131K |
| Agentic Coding | B | Runs well | 17.9 tok/s | 15776 ms | 131K |
| Reasoning | B | Runs well | 17.9 tok/s | 12818 ms | 131K |
| RAG | B | Runs well | 17.9 tok/s | 19720 ms | 131K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B55 |
Q3_K_S | 3 | 6.4 GB | Low | B55 |
NVFP4 | 4 | 7.3 GB | Medium | B55 |
Q4_K_M | 4 | 7.9 GB | Medium | B56 |
Q5_K_M | 5 | 9.4 GB | High | B56 |
Q6_K | 6 | 10.7 GB | High | B56 |
Q8_0 | 8 | 13.9 GB | Very High | B56 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B58 |
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 99Upgrade-Optionen
NVIDIA H200 141GBGünstige WahlRaises estimated decode speed by about 917%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA H200 PCIe 141GBBestes Preis-Leistungs-VerhältnisRaises estimated decode speed by about 917%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA B200 180GBNVIDIA-UpgradeRaises estimated decode speed by about 917%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 17.9 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 33.4 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 NVIDIA DGX Spark 128GB, Cerebras-GPT 13B achieves approximately 17.9 tokens per second decode speed with a time-to-first-token of 10846ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on NVIDIA DGX Spark 128GB receives a B grade with 17.9 tok/s and 131K context.
On NVIDIA DGX Spark 128GB, Cerebras-GPT 13B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Not always. NVIDIA DGX Spark 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/cerebras-gpt-13b-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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