48 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.21.2 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
~$4,650 MSRP
Can Qwen3-Coder-Next run on NVIDIA A100 40GB?
YES — With Q3_K_S
A82Great
○Estimated from fit model
Qwen3-Coder-Next needs ~45.9 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q3_K_S quantization, expect ~57 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload
Share:
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.
Qwen3-Coder-Next at Q4_K_M needs 55.5 GB — too much for NVIDIA A100 40GB (40.0 GB). Runs at Q3_K_S (45.9 GB) with low quality. 2 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 55.5 GB, exceeds 40.0 GB available
55.5 GB required40.0 GB available
139% VRAM needed
15.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
33.2 tok/s
TTFT
5823 ms
Safe context
4K
Memory
55.5 GB / 40.0 GB
Offload
30%
Memory breakdown
Weights48.8 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom4.0 GB
See how fast it feels
With memory offload — actual speed may be lowerSee how fast it feelsQwen3-Coder-Next on NVIDIA A100 40GB
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: 33.2 tok/s decode · 5.8s TTFT (warm) · 83 tok/s prefill
What limits this setup
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 5.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 31.4 tok/s | 3359 ms | 4K |
| Coding | F | Too heavy | 30.6 tok/s | 6333 ms | 4K |
| Agentic Coding | F | Too heavy | 28.9 tok/s | 9731 ms | 4K |
| Reasoning | F | Too heavy | 30.6 tok/s | 7484 ms | 4K |
| RAG | F | Too heavy | 28.9 tok/s | 12163 ms | 4K |
Quantization options
How Qwen3-Coder-Next (80B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 31.2 GB | Low | F0 |
Q3_K_S | 3 | 39.2 GB | Low | F0 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Qwen3-Coder-Next on your machine.
Run
ollama run qwen3-coder-nextUpgrade options
Hardware that runs Qwen3-Coder-Next well
48 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.42.2 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 27%.
~$4,999 MSRP
64 GB VRAM (+24)
S
Makes the model fit on the accelerator instead of staying completely out of reach.31.6 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$6,500 MSRP
80 GB VRAM (+40)2039 GB/s (+484)
S
Makes the model fit on the accelerator instead of staying completely out of reach.115.7 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP