Can Command R+ 104B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

B69Good

Estimated from fit model

Command R+ 104B needs ~80.6 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 80.6 GB, 34.6 tok/s, Runs well

80.6 GB required128.0 GB available

63% VRAM used

Fit status

Runs well

Decode

34.6 tok/s

TTFT

5602 ms

Safe context

131K

Memory

80.6 GB / 128.0 GB

Memory breakdown

Weights63.4 GB

KV Cache3.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsCommand R+ 104B on Intel Data Center GPU Max 1550 128GB
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: 34.6 tok/s decode · 5.6s TTFT (warm) · 86 tok/s prefill

What limits this setup

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.

Best improvement path

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	34.6 tok/s	3056 ms	131K
Coding	B	Runs well	34.6 tok/s	5602 ms	131K
Agentic Coding	A	Runs well	34.6 tok/s	8148 ms	131K
Reasoning	B	Runs well	34.6 tok/s	6621 ms	131K
RAG	A	Runs well	34.6 tok/s	10186 ms	131K

Quantization options

How Command R+ 104B (104B params) fits at each quantization level on Intel Data Center GPU Max 1550 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	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

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

NVIDIA H200 141GBコスパ重視

141 GB VRAM (+13)4800 GB/s (+1600)

Raises estimated decode speed by about 100%.69.1 tok/sデコード

Raises estimated decode speed by about 100%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$30,000 MSRP

NVIDIA H200 PCIe 141GBコスパ最良

141 GB VRAM (+13)4800 GB/s (+1600)

Raises estimated decode speed by about 100%.69.1 tok/sデコード

Raises estimated decode speed by about 100%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$30,000 MSRP

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Command R+ 104B?

Yes, Intel Data Center GPU Max 1550 128GB can run Command R+ 104B with a B grade (Runs well). Expected decode speed: 34.6 tok/s.

How much VRAM does Command R+ 104B need?

Command R+ 104B (104B parameters) requires approximately 80.6 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 Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Command R+ 104B achieves approximately 34.6 tokens per second decode speed with a time-to-first-token of 5602ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Command R+ 104B for coding?

For coding workloads, Command R+ 104B on Intel Data Center GPU Max 1550 128GB receives a B grade with 34.6 tok/s and 131K context.

What context window can Command R+ 104B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 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.

What should I upgrade first if Command R+ 104B feels slow on Intel Data Center GPU Max 1550 128GB?

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.

Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for Command R+ 104B?

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.

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