Qwen3-Coder-Next needs ~56.0 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~43 tok/s.
Operating mode
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
8.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~6.9 GB host RAM)
Decode
42.6 tok/s
TTFT
4542 ms
Safe context
4K
Memory
56.0 GB / 48.0 GB
Offload
10%
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.
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 6.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Very compromised (needs ~6.4 GB host RAM) | 43.8 tok/s | 2412 ms | 4K |
| Coding | A | Very compromised (needs ~6.9 GB host RAM) | 42.6 tok/s | 4542 ms | 4K |
| Agentic Coding | A | Very compromised (needs ~8 GB host RAM) | 40.4 tok/s | 6964 ms | 4K |
| Reasoning | A | Very compromised (needs ~6.9 GB host RAM) | 42.6 tok/s | 5368 ms | 4K |
| RAG | A | Very compromised (needs ~8 GB host RAM) | 40.4 tok/s | 8705 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Qwen3-Coder-Next at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~49 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? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 48.9 | Fits |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 43.1 | Heavy offload |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 40.7 | Fits |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 39.3 | Heavy offload |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 38.6 | Fits |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 33.6 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 30.2 | Fits |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 29.6 | Heavy offload |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 21.7 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 20.8 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 16.6 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 15.3 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 10.8 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 7.8 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 7.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 6.6 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 6.2 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 3.8 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.4 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | 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.
How Qwen3-Coder-Next (80B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 31.2 GB | Low | S88 |
Q3_K_S | 3 | 39.2 GB | Low | F0 |
NVFP4 | 4 | 44.8 GB | Medium | F0 |
Q4_K_M | 4 | 48.8 GB | Medium | F0 |
Q5_K_M | 5 | 57.6 GB | High | F0 |
Q6_K | 6 | 65.6 GB | High | F0 |
Q8_0 | 8 | 85.6 GB | Very High | F0 |
F16 | 16 | 164.0 GB | Maximum | F0 |
Copy-paste commands to run Qwen3-Coder-Next on your machine.
Run
ollama run qwen3-coder-nextYes, RTX PRO 5000 Blackwell 48GB can run Qwen3-Coder-Next with a A grade (Very compromised (needs ~6.9 GB host RAM)). Expected decode speed: 42.6 tok/s.
Qwen3-Coder-Next (80B parameters) requires approximately 56.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.
On RTX PRO 5000 Blackwell 48GB, Qwen3-Coder-Next achieves approximately 42.6 tokens per second decode speed with a time-to-first-token of 4542ms using Q4_K_M quantization.
For coding workloads, Qwen3-Coder-Next on RTX PRO 5000 Blackwell 48GB receives a A grade with 42.6 tok/s and 4K context.
On RTX PRO 5000 Blackwell 48GB, Qwen3-Coder-Next can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-3-coder-next-on-rtx-pro-5000-blackwell-48gb" 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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