Solar 7B needs ~9.2 GB VRAM. RTX 4070 Laptop 8GB has 8.0 GB. With Q4_K_M 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
1.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.6 GB host RAM)
Decode
27.4 tok/s
TTFT
7072 ms
Safe context
8K
Memory
9.2 GB / 8.0 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 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload | 49.0 tok/s | 2156 ms | 8K |
| Coding | B | Very compromised (needs ~0.6 GB host RAM) | 27.4 tok/s | 7072 ms | 8K |
| Agentic Coding | F | Too heavy | 15.3 tok/s | 18408 ms | 8K |
| Reasoning | B | Very compromised (needs ~0.6 GB host RAM) | 27.4 tok/s | 8358 ms | 8K |
| RAG | F | Too heavy | 15.3 tok/s | 23010 ms |
How Solar 7B (7B params) fits at each quantization level on RTX 4070 Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A74 |
Q3_K_S | 3 | 3.4 GB | Low | A74 |
NVFP4 | 4 |
Copy-paste commands to run Solar 7B on your machine.
Run
lms load Solar-7B && lms server startUpgrade options
RTX 3060 12GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 118%.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 155%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 93%.
~$499 MSRP
Yes, RTX 4070 Laptop 8GB can run Solar 7B with a B grade (Very compromised (needs ~0.6 GB host RAM)). Expected decode speed: 27.4 tok/s.
Solar 7B (7B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Solar 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4070 Laptop 8GB, Solar 7B achieves approximately 27.4 tokens per second decode speed with a time-to-first-token of 7072ms using Q4_K_M quantization.
For coding workloads, Solar 7B on RTX 4070 Laptop 8GB receives a B grade with 27.4 tok/s and 8K context.
On RTX 4070 Laptop 8GB, Solar 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/solar-7b-on-rtx-4070-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:
| 8K |
| Medium |
| A74 |
Q4_K_M | 4 | 4.3 GB | Medium | A74 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | A73 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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.