Command R+ 104B needs ~80.6 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~41 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
44.4 tok/s
TTFT
4361 ms
Safe context
131K
Memory
80.6 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 44.4 tok/s | 2378 ms | 131K |
| Coding | A | Runs well | 40.8 tok/s | 4742 ms | 131K |
| Agentic Coding | A | Runs well | 44.4 tok/s | 6343 ms | 131K |
| Reasoning | A | Runs well | 44.4 tok/s | 5153 ms | 131K |
| RAG | A | Runs well | 44.4 tok/s | 7928 ms | 131K |
How Command R+ 104B (104B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 40.6 GB | Low | B60 |
Q3_K_S | 3 | 51.0 GB | Low | B62 |
NVFP4 | 4 |
Copy-paste commands to run Command R+ 104B on your machine.
Run
ollama run command-r-plusYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 37.5 tok/s | |
 Qwen 3.5 122B A10B | 122B | S |
Yes, Gaudi 3 128GB can run Command R+ 104B with a A grade (Runs well). Expected decode speed: 40.8 tok/s.
Command R+ 104B (104B parameters) requires approximately 80.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Command R+ 104B is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Command R+ 104B achieves approximately 40.8 tokens per second decode speed with a time-to-first-token of 4742ms using Q4_K_M quantization.
For coding workloads, Command R+ 104B on Gaudi 3 128GB receives a A grade with 40.8 tok/s and 131K context.
On Gaudi 3 128GB, Command R+ 104B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/command-r-plus-104b-on-gaudi-3-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
58.2 GB |
| Medium |
| B63 |
Q4_K_M | 4 | 63.4 GB | Medium | B64 |
Q5_K_M | 5 | 74.9 GB | High | B65 |
Q6_KBest for your GPU | 6 | 85.3 GB | High | B65 |
Q8_0 | 8 | 111.3 GB | Very High | F0 |
F16 | 16 | 213.2 GB | Maximum | F0 |
| 104.1 tok/s |
| Mistral Small 4 119B | 119B | S | 112.9 tok/s |
 GPT-OSS 120B | 117B | S | 39.5 tok/s |
 Command A 111B | 111B | S | 41.8 tok/s |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.