96 GB VRAM (+16)
A
This setup is broadly balanced for this model.25.8 tok/s decode
~$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 ~44 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: Balanced
Share:
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 | 44.4 tok/s | 2381 ms | 36K |
| Coding | B | Tight fit | 44.4 tok/s | 4365 ms | 36K |
| Agentic Coding | B | Runs with offload | 44.4 tok/s | 6349 ms | 36K |
| Reasoning | B | Tight fit | 44.4 tok/s | 5158 ms | 36K |
| RAG | B | Runs with offload | 44.4 tok/s | 7936 ms | 36K |
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 |
Get started
Copy-paste commands to run Command R+ 104B on your machine.
Run
ollama run command-r-plusUpgrade options