Will It Run AI

Can Codestral RAG 19B Pruned i1 run on Gaudi 3 128GB?

YES — Runs Great

C46Usable
Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~27.5 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~224 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 27.5 GB, 223.5 tok/s, Runs well
27.5 GB required128.0 GB available
21% VRAM used

Fit status

Runs well

Decode

223.5 tok/s

TTFT

866 ms

Safe context

738K

Memory

27.5 GB / 128.0 GB

Memory breakdown

Weights11.6 GB
KV Cache2.2 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on Gaudi 3 128GB
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: 223.5 tok/s decode · 866ms TTFT (warm) · 559 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 well223.5 tok/s473 ms738K
CodingCRuns well223.5 tok/s866 ms738K
Agentic CodingCRuns well223.5 tok/s1260 ms738K
ReasoningCRuns well223.5 tok/s1024 ms738K
RAGCRuns well223.5 tok/s1575 ms738K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.4 GB
LowD38
Q3_K_S
3
9.3 GB
LowD38
NVFP4
4
10.6 GB
MediumD38
Q4_K_M
4
11.6 GB
MediumD38
Q5_K_M
5
13.7 GB
HighD38
Q6_K
6
15.6 GB
HighD38
Q8_0
8
20.3 GB
Very HighD39
F16Best for your GPU
16
38.9 GB
MaximumC41

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

Frequently asked questions

Can Gaudi 3 128GB run Codestral RAG 19B Pruned i1?

Yes, Gaudi 3 128GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 223.5 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 27.5 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 Gaudi 3 128GB?

On Gaudi 3 128GB, Codestral RAG 19B Pruned i1 achieves approximately 223.5 tokens per second decode speed with a time-to-first-token of 866ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Gaudi 3 128GB receives a C grade with 223.5 tok/s and 738K context.

What context window can Codestral RAG 19B Pruned i1 use on Gaudi 3 128GB?

On Gaudi 3 128GB, Codestral RAG 19B Pruned i1 can safely use up to 738K 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 Gaudi 3 128GB?

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 Gaudi 3 128GB 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 Gaudi 3 128GBSee all hardware for Codestral RAG 19B Pruned i1
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