Can Command R+ 104B run on NVIDIA H200 141GB?

YES — Runs Great

A71Great
Estimated from fit model

Command R+ 104B needs ~81.9 GB VRAM. NVIDIA H200 141GB has 141.0 GB. With Q4_K_M quantization, expect ~69 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) 81.9 GB, 69.1 tok/s, Runs well
81.9 GB required141.0 GB available
58% VRAM used

Fit status

Runs well

Decode

69.1 tok/s

TTFT

2801 ms

Safe context

131K

Memory

81.9 GB / 141.0 GB

Memory breakdown

Weights63.4 GB
KV Cache3.4 GB
Runtime0.9 GB
Headroom14.1 GB

See how fast it feels

See how fast it feelsCommand R+ 104B on NVIDIA H200 141GB
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: 69.1 tok/s decode · 2.8s TTFT (warm) · 173 tok/s prefill

What limits this setup

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.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns well69.1 tok/s1528 ms131K
CodingARuns well69.1 tok/s2801 ms131K
Agentic CodingARuns well69.1 tok/s4074 ms131K
ReasoningARuns well69.1 tok/s3310 ms131K
RAGARuns well69.1 tok/s5093 ms131K

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 / MacMemoryQuantSpeed (tok/s)Fits?
MacBook Pro M4 Max 128GB
128 GBQ4_K_M10.3Tight
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M9.5Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M8.0Tight
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M7.5Tight
2× RX 7900 XTX 24GB
48 GBQ4_K_M6.3Too big
MacBook Pro M4 Max 64GB
64 GBQ4_K_M5.5Too big
NVIDIA2× RTX 4090 24GB
48 GBQ4_K_M4.4Too big
NVIDIA2× RTX 3090 24GB
48 GBQ4_K_M3.8Too big
NVIDIA4× RTX 3060 12GB
48 GBQ4_K_M3.3Too big
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M2.9Too big
MacBook Pro M3 Max 64GB
64 GBQ4_K_M2.2Too big
NVIDIARTX 5090 32GB
32 GBQ4_K_M2.0Too big
NVIDIARTX 4090 24GB
24 GBQ4_K_M2.0Too big
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M2.0Too big
NVIDIARTX 3090 24GB
24 GBQ4_K_M2.0Too big
NVIDIARTX 4070 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too big
RX 7900 XTX 24GB
24 GBQ4_K_M2.0Too big
MacBook Pro M1 Max 64GB
64 GBQ4_K_M2.0Too 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 H200 141GB (141.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
40.6 GB
LowB59
Q3_K_S
3
51.0 GB
LowB61
NVFP4
4
58.2 GB
MediumB62
Q4_K_M
4
63.4 GB
MediumB63
Q5_K_M
5
74.9 GB
HighB65
Q6_K
6
85.3 GB
HighB65
Q8_0Best for your GPU
8
111.3 GB
Very HighB65
F16
16
213.2 GB
MaximumF0

Get started

Copy-paste commands to run Command R+ 104B on your machine.

Run

ollama run command-r-plus

Your hardware

More models your NVIDIA H200 141GB can run

ModelParamsGradeDecodeCapabilities
MistralDevstral 2 123B Instruct123BS58.4 tok/s
AlibabaQwen 3.5 122B A10B122BS162.1 tok/s
MistralMistral Small 4 119B119BS175.8 tok/s
OpenAIGPT-OSS 120B117BS61.4 tok/s
CohereCommand A 111B111BS65 tok/s

Frequently asked questions

Can NVIDIA H200 141GB run Command R+ 104B?

Yes, NVIDIA H200 141GB can run Command R+ 104B with a A grade (Runs well). Expected decode speed: 69.1 tok/s.

How much VRAM does Command R+ 104B need?

Command R+ 104B (104B parameters) requires approximately 81.9 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 H200 141GB?

On NVIDIA H200 141GB, Command R+ 104B achieves approximately 69.1 tokens per second decode speed with a time-to-first-token of 2801ms using Q4_K_M quantization.

Can NVIDIA H200 141GB run Command R+ 104B for coding?

For coding workloads, Command R+ 104B on NVIDIA H200 141GB receives a A grade with 69.1 tok/s and 131K context.

What context window can Command R+ 104B use on NVIDIA H200 141GB?

On NVIDIA H200 141GB, 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.

See all results for NVIDIA H200 141GBSee all hardware for Command R+ 104B
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