Radeon Pro W7900 48GBコスパ重視
48 GB VRAM (+16)864 GB/s (+352)
C
Raises estimated decode speed by about 78%.92.9 tok/sデコード
Raises estimated decode speed by about 78%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Can Yi Coder 9B Chat run on Radeon Pro W6800 32GB?
YES — Runs Great
C47Usable
Estimated from fit model
Yi Coder 9B Chat needs ~10.6 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 10.6 GB, 52.2 tok/s, Runs well
10.6 GB required32.0 GB available
33% VRAM used
Fit status
Runs well
Decode
52.2 tok/s
TTFT
3707 ms
Safe context
340K
Memory
10.6 GB / 32.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsYi Coder 9B Chat on Radeon Pro W6800 32GB
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: 52.2 tok/s decode · 3.7s TTFT (warm) · 131 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 | 52.2 tok/s | 2022 ms | 340K |
| Coding | C | Runs well | 52.2 tok/s | 3707 ms | 340K |
| Agentic Coding | C | Runs well | 52.2 tok/s | 5392 ms | 340K |
| Reasoning | C | Runs well | 52.2 tok/s | 4381 ms | 340K |
| RAG | C | Runs well | 52.2 tok/s | 6740 ms | 340K |
Quantization options
How Yi Coder 9B Chat (9B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C43 |
Q3_K_S | 3 | 4.4 GB | Low | C44 |
NVFP4 | 4 | 5.0 GB | Medium | C44 |
Q4_K_M | 4 | 5.5 GB | Medium | C44 |
Q5_K_M | 5 | 6.5 GB | High | C44 |
Q6_K | 6 | 7.4 GB | High | C45 |
Q8_0 | 8 | 9.6 GB | Very High | C46 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C49 |
Get started
Copy-paste commands to run Yi Coder 9B Chat on your machine.
Run
lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server startアップグレードオプション
Yi Coder 9B Chatを快適に動かすハードウェア
Frequently asked questions
Can Radeon Pro W6800 32GB run Yi Coder 9B Chat?
Yes, Radeon Pro W6800 32GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 52.2 tok/s.
How much VRAM does Yi Coder 9B Chat need?
Yi Coder 9B Chat (9B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi Coder 9B Chat?
The recommended quantization for Yi Coder 9B Chat is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi Coder 9B Chat run at on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Yi Coder 9B Chat achieves approximately 52.2 tokens per second decode speed with a time-to-first-token of 3707ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run Yi Coder 9B Chat for coding?
For coding workloads, Yi Coder 9B Chat on Radeon Pro W6800 32GB receives a C grade with 52.2 tok/s and 340K context.
What context window can Yi Coder 9B Chat use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Yi Coder 9B Chat can safely use up to 340K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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