RTX 4000 Ada 20GBコスパ重視20 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.24.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,250 MSRP
Can Codestral RAG 19B Pruned i1 run on RTX A4000 16GB?
YES — With Offload
C48Usable
Estimated from fit model
Codestral RAG 19B Pruned i1 needs ~16.3 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~20 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 16.3 GB, 19.5 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
19.5 tok/s
TTFT
9942 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 RTX A4000 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: 19.5 tok/s decode · 9.9s TTFT (warm) · 49 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Best improvement 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 | 27.1 tok/s | 3903 ms | 14K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 19.5 tok/s | 9942 ms | 14K |
| Agentic Coding | D | Very compromised (needs ~1.6 GB host RAM) | 14.9 tok/s | 18929 ms | 14K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 19.5 tok/s | 11749 ms | 14K |
| RAG | D | Very compromised (needs ~1.6 GB host RAM) | 14.9 tok/s | 23661 ms | 14K |
Quantization options
How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on RTX A4000 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 | 10.6 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 11.6 GB | Medium | C50 |
Q5_K_M | 5 | 13.7 GB | High | F0 |
Q6_K | 6 | 15.6 GB | High | F0 |
Q8_0 | 8 | 20.3 GB | Very High | F0 |
F16 | 16 | 38.9 GB | Maximum | F0 |
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アップグレードオプション
Codestral RAG 19B Pruned i1を快適に動かすハードウェア
RTX 4000 Ada 20GBコスパ重視RTX 3090 24GBコスパ最良24 GB VRAM (+8)936 GB/s (+488)
C
Raises estimated decode speed by about 160%.50.7 tok/sデコード
Raises estimated decode speed by about 160%.
Adds memory headroom for longer context windows and future model growth.
〜$1,499 MSRP
RTX 4090 24GBNVIDIAアップグレード24 GB VRAM (+8)1008 GB/s (+560)
C
Raises estimated decode speed by about 183%.55.2 tok/sデコード
Raises estimated decode speed by about 183%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can RTX A4000 16GB run Codestral RAG 19B Pruned i1?
Yes, RTX A4000 16GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 19.5 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 RTX A4000 16GB?
On RTX A4000 16GB, Codestral RAG 19B Pruned i1 achieves approximately 19.5 tokens per second decode speed with a time-to-first-token of 9942ms using Q4_K_M quantization.
Can RTX A4000 16GB run Codestral RAG 19B Pruned i1 for coding?
For coding workloads, Codestral RAG 19B Pruned i1 on RTX A4000 16GB receives a C grade with 19.5 tok/s and 14K context.
What context window can Codestral RAG 19B Pruned i1 use on RTX A4000 16GB?
On RTX A4000 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.
What should I upgrade first if Codestral RAG 19B Pruned i1 feels slow on RTX A4000 16GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--codestral-rag-19b-pruned-i1-gguf-on-a4000-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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