Can Codestral RAG 19B Pruned i1 run on Quadro RTX 8000 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~19.8 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 19.8 GB, 40.0 tok/s, Runs well

19.8 GB required48.0 GB available

41% VRAM used

Fit status

Runs well

Decode

40.0 tok/s

TTFT

4839 ms

Safe context

219K

Memory

19.8 GB / 48.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on Quadro RTX 8000 48GB
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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	40.0 tok/s	2640 ms	219K
Coding	C	Runs well	40.0 tok/s	4839 ms	219K
Agentic Coding	C	Runs well	40.0 tok/s	7039 ms	219K
Reasoning	C	Runs well	40.0 tok/s	5719 ms	219K
RAG	C	Runs well	40.0 tok/s	8798 ms	219K

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 Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C42
Q3_K_S	3	9.3 GB	Low	C42
NVFP4	4	10.6 GB	Medium	C43
Q4_K_M	4	11.6 GB	Medium	C43
Q5_K_M	5	13.7 GB	High	C43
Q6_K	6	15.6 GB	High	C44
Q8_0	8	20.3 GB	Very High	C46
F16Best for your GPU	16	38.9 GB	Maximum	C47

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

升级选项

能流畅运行 Codestral RAG 19B Pruned i1 的硬件

AMD Instinct MI210 64GB经济之选

64 GB VRAM (+16)1638 GB/s (+966)

Raises estimated decode speed by about 140%.96.1 tok/s 解码

Raises estimated decode speed by about 140%.

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

~$10,000 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run Codestral RAG 19B Pruned i1?

Yes, Quadro RTX 8000 48GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 40.0 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 19.8 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Codestral RAG 19B Pruned i1 achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4839ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Quadro RTX 8000 48GB receives a C grade with 40.0 tok/s and 219K context.

What context window can Codestral RAG 19B Pruned i1 use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Codestral RAG 19B Pruned i1 can safely use up to 219K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GB See all hardware for Codestral RAG 19B Pruned i1

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