Can Command R 35B run on Intel Arc Pro B60 24GB?

BARELY — Tight on Memory

B62Good

Estimated from fit model

Command R 35B needs ~27.1 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 27.1 GB, 7.5 tok/s, Very compromised (needs ~2.4 GB host RAM)

27.1 GB required24.0 GB available

113% VRAM needed

3.1 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.4 GB host RAM)

Decode

7.5 tok/s

TTFT

25844 ms

Safe context

Memory

27.1 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights21.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCommand R 35B on Intel Arc Pro B60 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 7.5 tok/s decode · 25.8s TTFT (warm) · 19 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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.

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	Runs with offload (needs ~1.5 GB host RAM)	8.2 tok/s	12831 ms	4K
Coding	B	Very compromised (needs ~2.4 GB host RAM)	7.5 tok/s	25844 ms	4K
Agentic Coding	F	Too heavy	6.3 tok/s	44827 ms	4K
Reasoning	B	Very compromised (needs ~2.4 GB host RAM)	7.5 tok/s	30543 ms	4K
RAG	F	Too heavy	6.3 tok/s	56034 ms	4K

Quantization options

How Command R 35B (35B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A76
Q3_K_SBest for your GPU	3	17.2 GB	Low	A76
NVFP4	4	19.6 GB	Medium	F0
Q4_K_M	4	21.3 GB	Medium	F0
Q5_K_M	5	25.2 GB	High	F0
Q6_K	6	28.7 GB	High	F0
Q8_0	8	37.5 GB	Very High	F0
F16	16	71.8 GB	Maximum	F0

Get started

Copy-paste commands to run Command R 35B on your machine.

Run

ollama run command-r

Opções de upgrade

Hardware que roda bem Command R 35B

NVIDIA A100 40GBMaior salto

40 GB VRAM (+16)1555 GB/s (+1099)

Removes host-memory offload, which is usually the single biggest latency and throughput win.66.5 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 787%.

~$10,000 MSRP

Intel Data Center GPU Max 1550 128GBOpção econômica

128 GB VRAM (+104)3200 GB/s (+2744)

Removes host-memory offload, which is usually the single biggest latency and throughput win.102.7 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 1269%.

~$15,000 MSRP

Gaudi 3 128GBMelhor custo-benefício

128 GB VRAM (+104)3700 GB/s (+3244)

Removes host-memory offload, which is usually the single biggest latency and throughput win.131.9 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 1659%.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Command R 35B?

Yes, Intel Arc Pro B60 24GB can run Command R 35B with a B grade (Very compromised (needs ~2.4 GB host RAM)). Expected decode speed: 7.5 tok/s.

How much VRAM does Command R 35B need?

Command R 35B (35B parameters) requires approximately 27.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Command R 35B?

The recommended quantization for Command R 35B is Q4_K_M, which balances quality and memory efficiency.

What speed will Command R 35B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Command R 35B achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25844ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Command R 35B for coding?

For coding workloads, Command R 35B on Intel Arc Pro B60 24GB receives a B grade with 7.5 tok/s and 4K context.

What context window can Command R 35B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Command R 35B can safely use up to 4K 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 35B feels slow on Intel Arc Pro B60 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Command R 35B?

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.

See all results for Intel Arc Pro B60 24GB See all hardware for Command R 35B

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