RX 7900 XT 20GB经济之选
20 GB VRAM (+4)800 GB/s (+288)
C
Raises estimated decode speed by about 133%.41.4 tok/s 解码
Raises estimated decode speed by about 133%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Can Codestral RAG 19B Pruned i1 run on RX 6800 XT 16GB?
YES — With Offload
C48Usable
Estimated from fit model
Codestral RAG 19B Pruned i1 needs ~16.3 GB VRAM. RX 6800 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~18 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.
Select quantization to explore
Q4_K_M (Medium quality) — 16.3 GB, 17.8 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
17.8 tok/s
TTFT
10874 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 RX 6800 XT 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: 17.8 tok/s decode · 10.9s TTFT (warm) · 45 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 | 24.7 tok/s | 4269 ms | 14K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 17.8 tok/s | 10874 ms | 14K |
| Agentic Coding | D | Very compromised (needs ~1.6 GB host RAM) | 13.6 tok/s | 20704 ms | 14K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 17.8 tok/s | 12851 ms | 14K |
| RAG | D | Very compromised (needs ~1.6 GB host RAM) | 13.6 tok/s | 25880 ms | 14K |
Quantization options
How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on RX 6800 XT 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升级选项
能流畅运行 Codestral RAG 19B Pruned i1 的硬件
RX 7900 XTX 24GB最佳性价比
24 GB VRAM (+8)960 GB/s (+448)
C
Raises estimated decode speed by about 235%.59.6 tok/s 解码
Raises estimated decode speed by about 235%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Radeon AI PRO R9700 32GBAMD 升级
32 GB VRAM (+16)640 GB/s (+128)
C
Raises estimated decode speed by about 83%.32.6 tok/s 解码
Raises estimated decode speed by about 83%.
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Frequently asked questions
Can RX 6800 XT 16GB run Codestral RAG 19B Pruned i1?
Yes, RX 6800 XT 16GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 17.8 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 RX 6800 XT 16GB?
On RX 6800 XT 16GB, Codestral RAG 19B Pruned i1 achieves approximately 17.8 tokens per second decode speed with a time-to-first-token of 10874ms using Q4_K_M quantization.
Can RX 6800 XT 16GB run Codestral RAG 19B Pruned i1 for coding?
For coding workloads, Codestral RAG 19B Pruned i1 on RX 6800 XT 16GB receives a C grade with 17.8 tok/s and 14K context.
What context window can Codestral RAG 19B Pruned i1 use on RX 6800 XT 16GB?
On RX 6800 XT 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 RX 6800 XT 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-rx-6800-xt-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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