RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視96 GB VRAM (+16)
A
This setup is broadly balanced for this model.25.8 tok/sデコード
〜$9,999 MSRP
Can Command R+ 104B run on NVIDIA H100 80GB?
YES — Tight Fit
B69Good
Estimated from fit model
Command R+ 104B needs ~75.8 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~48 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 75.8 GB, 48.2 tok/s, Tight fit
75.8 GB required80.0 GB available
95% VRAM used
Fit status
Tight fit
Decode
48.2 tok/s
TTFT
4013 ms
Safe context
36K
Memory
75.8 GB / 80.0 GB
Memory breakdown
Weights63.4 GB
KV Cache3.4 GB
Runtime0.9 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsCommand R+ 104B on NVIDIA H100 80GB
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: 48.2 tok/s decode · 4.0s TTFT (warm) · 121 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 48.2 tok/s | 2189 ms | 36K |
| Coding | B | Tight fit | 48.2 tok/s | 4013 ms | 36K |
| Agentic Coding | B | Runs with offload | 48.2 tok/s | 5838 ms | 36K |
| Reasoning | B | Tight fit | 48.2 tok/s | 4743 ms | 36K |
| RAG | B | Runs with offload | 48.2 tok/s | 7297 ms | 36K |
Inference speed
Command R+ 104B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Command R+ 104B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 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? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 10.3 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 9.5 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 8.0 | Tight |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 7.5 | Tight |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 6.3 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 5.5 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 4.4 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 3.8 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 3.3 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.9 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.2 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 2.0 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.0 | 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 Command R+ 104B (104B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 40.6 GB | Low | B65 |
Q3_K_S | 3 | 51.0 GB | Low | B65 |
NVFP4 | 4 | 58.2 GB | Medium | B65 |
Q4_K_MBest for your GPU | 4 | 63.4 GB | Medium | B65 |
Q5_K_M | 5 | 74.9 GB | High | F0 |
Q6_K | 6 | 85.3 GB | High | F0 |
Q8_0 | 8 | 111.3 GB | Very High | F0 |
F16 | 16 | 213.2 GB | Maximum | F0 |
Get started
Copy-paste commands to run Command R+ 104B on your machine.
Run
ollama run command-r-plusアップグレードオプション
Command R+ 104Bを快適に動かすハードウェア
RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視RTX PRO 6000 Blackwell Server Edition 96GBコスパ最良96 GB VRAM (+16)
B
This setup is broadly balanced for this model.23 tok/sデコード
〜$9,999 MSRP
NVIDIA H20 96GBNVIDIAアップグレード96 GB VRAM (+16)4000 GB/s (+650)
A
This setup is broadly balanced for this model.55.5 tok/sデコード
〜$12,000 MSRP
Frequently asked questions
Can NVIDIA H100 80GB run Command R+ 104B?
Yes, NVIDIA H100 80GB can run Command R+ 104B with a B grade (Tight fit). Expected decode speed: 48.2 tok/s.
How much VRAM does Command R+ 104B need?
Command R+ 104B (104B parameters) requires approximately 75.8 GB of memory with Q4_K_M quantization.
What is the best quantization for Command R+ 104B?
The recommended quantization for Command R+ 104B is Q4_K_M, which balances quality and memory efficiency.
What speed will Command R+ 104B run at on NVIDIA H100 80GB?
On NVIDIA H100 80GB, Command R+ 104B achieves approximately 48.2 tokens per second decode speed with a time-to-first-token of 4013ms using Q4_K_M quantization.
Can NVIDIA H100 80GB run Command R+ 104B for coding?
For coding workloads, Command R+ 104B on NVIDIA H100 80GB receives a B grade with 48.2 tok/s and 36K context.
What context window can Command R+ 104B use on NVIDIA H100 80GB?
On NVIDIA H100 80GB, Command R+ 104B can safely use up to 36K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
What should I upgrade first if Command R+ 104B feels slow on NVIDIA H100 80GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/command-r-plus-104b-on-h100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: