Can Codestral RAG 19B Pruned i1 run on NVIDIA H20 96GB?

YES — Runs Great

C47Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~24.6 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~266 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 24.6 GB, 266.0 tok/s, Runs well

24.6 GB required96.0 GB available

26% VRAM used

Fit status

Runs well

Decode

266.0 tok/s

TTFT

728 ms

Safe context

529K

Memory

24.6 GB / 96.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on NVIDIA H20 96GB
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: 266.0 tok/s decode · 728ms TTFT (warm) · 665 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	266.0 tok/s	397 ms	529K
Coding	C	Runs well	266.0 tok/s	728 ms	529K
Agentic Coding	C	Runs well	266.0 tok/s	1059 ms	529K
Reasoning	C	Runs well	266.0 tok/s	860 ms	529K
RAG	C	Runs well	266.0 tok/s	1323 ms	529K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	D39
Q3_K_S	3	9.3 GB	Low	D39
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

Frequently asked questions

Can NVIDIA H20 96GB run Codestral RAG 19B Pruned i1?

Yes, NVIDIA H20 96GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 266.0 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 24.6 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 NVIDIA H20 96GB?

On NVIDIA H20 96GB, Codestral RAG 19B Pruned i1 achieves approximately 266.0 tokens per second decode speed with a time-to-first-token of 728ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on NVIDIA H20 96GB receives a C grade with 266.0 tok/s and 529K context.

What context window can Codestral RAG 19B Pruned i1 use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Codestral RAG 19B Pruned i1 can safely use up to 529K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H20 96GB See all hardware for Codestral RAG 19B Pruned i1

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