Will It Run AI

Can Codestral 21B Pruned i1 run on Intel Arc Pro B60 24GB?

YES — Runs Great

C51Usable
Estimated from fit model

Codestral 21B Pruned i1 needs ~18.6 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 18.6 GB, 19.2 tok/s, Runs well
18.6 GB required24.0 GB available
78% VRAM used

Fit status

Runs well

Decode

19.2 tok/s

TTFT

10072 ms

Safe context

51K

Memory

18.6 GB / 24.0 GB

Memory breakdown

Weights12.8 GB
KV Cache2.5 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 on Intel Arc Pro B60 24GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 19.2 tok/s decode · 10.1s TTFT (warm) · 48 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well19.2 tok/s5494 ms51K
CodingCRuns well19.2 tok/s10072 ms51K
Agentic CodingCTight fit19.2 tok/s14650 ms51K
ReasoningCRuns well19.2 tok/s11903 ms51K
RAGCTight fit19.2 tok/s18312 ms51K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
8.2 GB
LowC47
Q3_K_S
3
10.3 GB
LowC49
NVFP4
4
11.8 GB
MediumC50
Q4_K_M
4
12.8 GB
MediumC50
Q5_K_M
5
15.1 GB
HighC49
Q6_KBest for your GPU
6
17.2 GB
HighC49
Q8_0
8
22.5 GB
Very HighF0
F16
16
43.1 GB
MaximumF0

Get started

Copy-paste commands to run Codestral 21B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server start

アップグレードオプション

Codestral 21B Pruned i1を快適に動かすハードウェア

NVIDIARTX 5090 32GBコスパ重視
32 GB VRAM (+8)1792 GB/s (+1336)
C
Raises estimated decode speed by about 331%.82.8 tok/sデコード

Raises estimated decode speed by about 331%.

Adds memory headroom for longer context windows and future model growth.

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

〜$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBコスパ最良
32 GB VRAM (+8)896 GB/s (+440)
C
Raises estimated decode speed by about 206%.58.8 tok/sデコード

Raises estimated decode speed by about 206%.

Adds memory headroom for longer context windows and future model growth.

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

〜$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Codestral 21B Pruned i1?

Yes, Intel Arc Pro B60 24GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 19.2 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 18.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization for Codestral 21B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 21B Pruned i1 run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Codestral 21B Pruned i1 achieves approximately 19.2 tokens per second decode speed with a time-to-first-token of 10072ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on Intel Arc Pro B60 24GB receives a C grade with 19.2 tok/s and 51K context.

What context window can Codestral 21B Pruned i1 use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Codestral 21B Pruned i1 can safely use up to 51K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 21B Pruned i1 feels slow on Intel Arc Pro B60 24GB?

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 Arc Pro B60 24GB for Codestral 21B Pruned i1?

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 24GBSee all hardware for Codestral 21B Pruned i1
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