Would CUDA be a better path than Intel Arc A770 16GB for Codestral 22B v0.1?

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.

Can Codestral 22B v0.1 run on Intel Arc A770 16GB?

Q: What should I upgrade first if Codestral 22B v0.1 feels slow on Intel Arc A770 16GB?

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.

BARELY — Tight on Memory

D37Poor

Estimated from fit model

Codestral 22B v0.1 needs ~18.5 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~10 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) — 18.5 GB, 10.4 tok/s, Very compromised (needs ~1.8 GB host RAM)

18.5 GB required16.0 GB available

116% VRAM needed

2.5 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.8 GB host RAM)

Decode

10.4 tok/s

TTFT

18656 ms

Safe context

Memory

18.5 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 on Intel Arc A770 16GB
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: 10.4 tok/s decode · 18.7s TTFT (warm) · 26 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	D	Runs with offload (needs ~0.9 GB host RAM)	12.1 tok/s	8741 ms	4K
Coding	D	Very compromised (needs ~1.8 GB host RAM)	10.4 tok/s	18656 ms	4K
Agentic Coding	F	Too heavy	7.9 tok/s	35714 ms	4K
Reasoning	D	Very compromised (needs ~1.8 GB host RAM)	10.4 tok/s	22048 ms	4K
RAG	F	Too heavy	7.9 tok/s	44642 ms

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C51
Q3_K_SBest for your GPU	3	10.8 GB	Low	C51

Get started

Copy-paste commands to run Codestral 22B v0.1 on your machine.

Run

lms load hf-sanctumai--codestral-22b-v0-1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 well

Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.18.3 tok/s decode

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

Raises estimated decode speed by about 76%.

~$599 MSRP

Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+112)3200 GB/s (+2640)

Removes host-memory offload, which is usually the single biggest latency and throughput win.150.2 tok/s decode

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

Raises estimated decode speed by about 1344%.

~$15,000 MSRP

Gaudi 3 128GBIntel upgrade

128 GB VRAM (+112)3700 GB/s (+3140)

Removes host-memory offload, which is usually the single biggest latency and throughput win.193 tok/s decode

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

Raises estimated decode speed by about 1756%.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run Codestral 22B v0.1?

Yes, Intel Arc A770 16GB can run Codestral 22B v0.1 with a D grade (Very compromised (needs ~1.8 GB host RAM)). Expected decode speed: 10.4 tok/s.

How much VRAM does Codestral 22B v0.1 need?

Codestral 22B v0.1 (22B parameters) requires approximately 18.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1?

The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Codestral 22B v0.1 achieves approximately 10.4 tokens per second decode speed with a time-to-first-token of 18656ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on Intel Arc A770 16GB receives a D grade with 10.4 tok/s and 4K context.

What context window can Codestral 22B v0.1 use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Codestral 22B v0.1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 22B v0.1 feels slow on Intel Arc A770 16GB?

See all results for Intel Arc A770 16GB See all hardware for Codestral 22B v0.1

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