RTX 3060 12GBBudget pick12 GB VRAM (+4)360 GB/s (+104)
C
Adds memory headroom for longer context windows and future model growth.50.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Neural Chat 7B needs ~7.9 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 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
Fit status
Runs with offload
Decode
43.3 tok/s
TTFT
4473 ms
Safe context
8K
Memory
7.9 GB / 8.0 GB
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 43.3 tok/s | 2440 ms | 8K |
| Coding | C | Runs with offload | 43.3 tok/s | 4473 ms | 8K |
| Agentic Coding | F | Too heavy | 20.8 tok/s | 13516 ms | 8K |
| Reasoning | C | Runs with offload | 43.3 tok/s | 5286 ms | 8K |
| RAG | F | Too heavy | 20.8 tok/s | 16895 ms | 8K |
How Neural Chat 7B (7B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C52 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 |
Copy-paste commands to run Neural Chat 7B on your machine.
Run
ollama run neural-chatUpgrade options
RTX 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRaises estimated decode speed by about 61%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, RTX 2000 Ada Laptop 8GB can run Neural Chat 7B with a C grade (Runs with offload). Expected decode speed: 43.3 tok/s.
Neural Chat 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Neural Chat 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2000 Ada Laptop 8GB, Neural Chat 7B achieves approximately 43.3 tokens per second decode speed with a time-to-first-token of 4473ms using Q4_K_M quantization.
For coding workloads, Neural Chat 7B on RTX 2000 Ada Laptop 8GB receives a C grade with 43.3 tok/s and 8K context.
On RTX 2000 Ada Laptop 8GB, Neural Chat 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/neural-chat-7b-on-rtx-2000-ada-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.