Will It Run AI

Can Solar 7B run on RTX 2070 8GB?

BARELY — Tight on Memory

B62Good
Estimated from fit model

Solar 7B needs ~8.9 GB VRAM. RTX 2070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) 8.9 GB, 39.1 tok/s, Very compromised (needs ~0.4 GB host RAM)
8.9 GB required8.0 GB available
111% VRAM needed

0.9 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

39.1 tok/s

TTFT

4956 ms

Safe context

8K

Memory

8.9 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsSolar 7B on RTX 2070 8GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 39.1 tok/s decode · 5.0s TTFT (warm) · 98 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatATight fit67.7 tok/s1560 ms8K
CodingBVery compromised36.3 tok/s5328 ms8K
Agentic CodingFToo heavy20.9 tok/s13468 ms8K
ReasoningBVery compromised (needs ~0.4 GB host RAM)39.1 tok/s5858 ms8K
RAGFToo heavy20.9 tok/s16834 ms8K

Quantization options

How Solar 7B (7B params) fits at each quantization level on RTX 2070 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowA74
Q3_K_S
3
3.4 GB
LowA74
NVFP4
4
3.9 GB
MediumA74
Q4_K_M
4
4.3 GB
MediumA74
Q5_K_MBest for your GPU
5
5.0 GB
HighA73
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

Get started

Copy-paste commands to run Solar 7B on your machine.

Run

lms load Solar-7B && lms server start

Opções de upgrade

Hardware que roda bem Solar 7B

Frequently asked questions

Can RTX 2070 8GB run Solar 7B?

Yes, RTX 2070 8GB can run Solar 7B with a B grade (Very compromised). Expected decode speed: 36.3 tok/s.

How much VRAM does Solar 7B need?

Solar 7B (7B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Solar 7B?

The recommended quantization for Solar 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar 7B run at on RTX 2070 8GB?

On RTX 2070 8GB, Solar 7B achieves approximately 36.3 tokens per second decode speed with a time-to-first-token of 5328ms using Q4_K_M quantization.

Can RTX 2070 8GB run Solar 7B for coding?

For coding workloads, Solar 7B on RTX 2070 8GB receives a B grade with 36.3 tok/s and 8K context.

What context window can Solar 7B use on RTX 2070 8GB?

On RTX 2070 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.

What should I upgrade first if Solar 7B feels slow on RTX 2070 8GB?

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.

See all results for RTX 2070 8GBSee all hardware for Solar 7B
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