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Neural Chat 7B needs ~7.2 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q3_K_S quantization, expect ~27 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
2.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
18.6 tok/s
TTFT
10420 ms
Safe context
4K
Memory
8.0 GB / 6.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~0.6 GB host RAM) | 24.7 tok/s | 4274 ms | 4K |
| Coding | F | Too heavy | 18.6 tok/s | 10420 ms | 4K |
| Agentic Coding | F | Too heavy | 11.5 tok/s | 24456 ms | 4K |
| Reasoning | F | Too heavy | 18.6 tok/s | 12314 ms | 4K |
| RAG | F | Too heavy | 11.5 tok/s | 30569 ms | 4K |
How Neural Chat 7B (7B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_SBest for your GPU | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | F0 |
Q4_K_M | 4 | 4.3 GB | Medium | F0 |
Q5_K_M | 5 | 5.0 GB | High | F0 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Copy-paste commands to run Neural Chat 7B on your machine.
Run
ollama run neural-chatUpgrade options
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.
~$249 MSRP
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.
~$299 MSRP
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.
~$299 MSRP
