Would CUDA be a better path than Intel Arc A770 16GB 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.

Can Codestral RAG 19B Pruned i1 run on Intel Arc A770 16GB?

Q: What should I upgrade first if Codestral RAG 19B Pruned i1 feels slow on Intel Arc A770 16GB?

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.

YES — With Offload

C48Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~16.3 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 16.3 GB, 15.6 tok/s, Runs with offload (needs ~0.2 GB host RAM)

16.3 GB required16.0 GB available

102% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

15.6 tok/s

TTFT

12372 ms

Safe context

14K

Memory

16.3 GB / 16.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 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: 15.6 tok/s decode · 12.4s TTFT (warm) · 39 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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	C	Runs with offload	21.7 tok/s	4857 ms	14K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	15.6 tok/s	12372 ms	14K
Agentic Coding	D	Very compromised (needs ~1.6 GB host RAM)	12.0 tok/s	23556 ms	14K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	15.6 tok/s	14621 ms	14K
RAG	D	Very compromised (needs ~1.6 GB host RAM)	12.0 tok/s

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C51
Q3_K_S	3	9.3 GB	Low	C51
NVFP4	4

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

Upgrade options

Hardware that runs Codestral RAG 19B Pruned i1 well

Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)

Raises estimated decode speed by about 36%.21.2 tok/s decode

Raises estimated decode speed by about 36%.

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

~$599 MSRP

MacBook Pro M4 32GBBest value

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.9.3 tok/s decode

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

~$799 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run Codestral RAG 19B Pruned i1?

Yes, Intel Arc A770 16GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 15.6 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 16.3 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 A770 16GB?

On Intel Arc A770 16GB, Codestral RAG 19B Pruned i1 achieves approximately 15.6 tokens per second decode speed with a time-to-first-token of 12372ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Intel Arc A770 16GB receives a C grade with 15.6 tok/s and 14K context.

What context window can Codestral RAG 19B Pruned i1 use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Codestral RAG 19B Pruned i1 can safely use up to 14K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc A770 16GB See all hardware for Codestral RAG 19B Pruned i1

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