Can Codestral RAG 19B Pruned i1 run on Intel Arc Pro B60 24GB?

YES — Runs Great

C52Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~17.1 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 17.1 GB, 21.2 tok/s, Runs well

17.1 GB required24.0 GB available

71% VRAM used

Fit status

Runs well

Decode

21.2 tok/s

TTFT

9112 ms

Safe context

65K

Memory

17.1 GB / 24.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B 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: 21.2 tok/s decode · 9.1s TTFT (warm) · 53 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	C	Runs well	21.2 tok/s	4970 ms	65K
Coding	C	Runs well	21.2 tok/s	9112 ms	65K
Agentic Coding	C	Runs well	21.2 tok/s	13254 ms	65K
Reasoning	C	Runs well	21.2 tok/s	10769 ms	65K
RAG	C	Runs well	21.2 tok/s	16568 ms	65K

Inference speed

Codestral RAG 19B Pruned i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral RAG 19B Pruned i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~104 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	103.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	66.1	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	59.6	Fits
RTX 3090 24GB	24 GB	Q4_K_M	56.5	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	48.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	40.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	38.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	36.5	Offloads
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	22.7	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	20.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	19.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	13.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	8.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	3.1	Too 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 Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C47
Q3_K_S	3	9.3 GB	Low	C48
NVFP4	4	10.6 GB	Medium	C49
Q4_K_M	4	11.6 GB	Medium	C49
Q5_K_M	5	13.7 GB	High	C50
Q6_KBest for your GPU	6	15.6 GB	High	C49
Q8_0	8	20.3 GB	Very High	F0
F16	16	38.9 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral RAG 19B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-rag-19b-pruned-i1-gguf && lms server start

Opções de upgrade

Hardware que roda bem Codestral RAG 19B Pruned i1

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1336)

Raises estimated decode speed by about 359%.97.3 tok/s decodificação

Raises estimated decode speed by about 359%.

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

MacBook Pro M4 Max 36GBMelhor custo-benefício

36 GB Unified (+12)

Raises estimated decode speed by about 35%.28.7 tok/s decodificação

Raises estimated decode speed by about 35%.

~$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Codestral RAG 19B Pruned i1?

Yes, Intel Arc Pro B60 24GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 21.2 tok/s.

How much VRAM does Codestral RAG 19B Pruned i1 need?

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 17.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral RAG 19B Pruned i1?

The recommended quantization for Codestral RAG 19B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral RAG 19B Pruned i1 run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Codestral RAG 19B Pruned i1 achieves approximately 21.2 tokens per second decode speed with a time-to-first-token of 9112ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Intel Arc Pro B60 24GB receives a C grade with 21.2 tok/s and 65K context.

What context window can Codestral RAG 19B Pruned i1 use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Codestral RAG 19B Pruned i1 can safely use up to 65K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral RAG 19B 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 RAG 19B 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 24GB See all hardware for Codestral RAG 19B Pruned i1

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