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⇱ Command R 35B on Intel Arc Pro B60 24GB? YES

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
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.

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

4K

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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload (needs ~1.5 GB host RAM)8.2 tok/s12831 ms4K
CodingBVery compromised (needs ~2.4 GB host RAM)7.5 tok/s25844 ms4K
Agentic CodingFToo heavy6.3 tok/s44827 ms4K
ReasoningBVery compromised (needs ~2.4 GB host RAM)7.5 tok/s30543 ms4K
RAGFToo heavy6.3 tok/s56034 ms4K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
13.7 GB
LowA76
Q3_K_SBest for your GPU
3
17.2 GB
LowA76
NVFP4
4
19.6 GB
MediumF0
Q4_K_M
4
21.3 GB
MediumF0
Q5_K_M
5
25.2 GB
HighF0
Q6_K
6
28.7 GB
HighF0
Q8_0
8
37.5 GB
Very HighF0
F16
16
71.8 GB
MaximumF0

Get started

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

Run

ollama run command-r

Upgrade options

Hardware that runs Command R 35B well

👁 NVIDIA
NVIDIA A100 40GBBiggest leap
40 GB VRAM (+16)1555 GB/s (+1099)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.66.5 tok/s decode

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
Intel Data Center GPU Max 1550 128GBBudget pick
128 GB VRAM (+104)3200 GB/s (+2744)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.102.7 tok/s decode

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

Raises estimated decode speed by about 1269%.

~$15,000 MSRP

👁 Intel
Gaudi 3 128GBBest value
128 GB VRAM (+104)3700 GB/s (+3244)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.131.9 tok/s decode

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

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