Can SOLAR 10.7B v1.0 run on RTX 3080 10GB?

Q: What should I upgrade first if SOLAR 10.7B v1.0 feels slow on RTX 3080 10GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C53Usable

Estimated from fit model

SOLAR 10.7B v1.0 needs ~10.0 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~89 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Balanced

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) — 10.0 GB, 88.5 tok/s, Runs with offload

10.0 GB required10.0 GB available

100% VRAM used

Fit status

Runs with offload

Decode

88.5 tok/s

TTFT

2188 ms

Safe context

16K

Memory

10.0 GB / 10.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsSOLAR 10.7B v1.0 on RTX 3080 10GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 88.5 tok/s decode · 2.2s TTFT (warm) · 221 tok/s prefill

What limits this setup

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.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	88.5 tok/s	1193 ms	16K
Coding	C	Runs with offload	88.5 tok/s	2188 ms	16K
Agentic Coding	C	Very compromised (needs ~0.7 GB host RAM)	51.9 tok/s	5421 ms	16K
Reasoning	C	Runs with offload	88.5 tok/s	2585 ms	16K
RAG	C	Very compromised (needs ~0.7 GB host RAM)	51.9 tok/s	6777 ms

Quantization options

How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C53
Q3_K_S	3	5.2 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run SOLAR 10.7B v1.0 on your machine.

Run

lms load hf-mradermacher--solar-10-7b-v1-0-gguf && lms server start

Upgrade options

Hardware that runs SOLAR 10.7B v1.0 well

RTX 5070 Ti 16GBBudget pick

16 GB VRAM (+6)896 GB/s (+136)

Adds memory headroom for longer context windows and future model growth.87.9 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$749 MSRP

RTX 4070 Ti Super 16GBBest value

16 GB VRAM (+6)

Adds memory headroom for longer context windows and future model growth.82.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$799 MSRP

RTX 3080 12GBNVIDIA upgrade

12 GB VRAM (+2)912 GB/s (+152)

This setup is broadly balanced for this model.106.2 tok/s decode

~$799 MSRP

Frequently asked questions

Can RTX 3080 10GB run SOLAR 10.7B v1.0?

Yes, RTX 3080 10GB can run SOLAR 10.7B v1.0 with a C grade (Runs with offload). Expected decode speed: 88.5 tok/s.

How much VRAM does SOLAR 10.7B v1.0 need?

SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 10.0 GB of memory with Q4_K_M quantization.

What is the best quantization for SOLAR 10.7B v1.0?

The recommended quantization for SOLAR 10.7B v1.0 is Q4_K_M, which balances quality and memory efficiency.

What speed will SOLAR 10.7B v1.0 run at on RTX 3080 10GB?

On RTX 3080 10GB, SOLAR 10.7B v1.0 achieves approximately 88.5 tokens per second decode speed with a time-to-first-token of 2188ms using Q4_K_M quantization.

Can RTX 3080 10GB run SOLAR 10.7B v1.0 for coding?

For coding workloads, SOLAR 10.7B v1.0 on RTX 3080 10GB receives a C grade with 88.5 tok/s and 16K context.

What context window can SOLAR 10.7B v1.0 use on RTX 3080 10GB?

On RTX 3080 10GB, SOLAR 10.7B v1.0 can safely use up to 16K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if SOLAR 10.7B v1.0 feels slow on RTX 3080 10GB?

See all results for RTX 3080 10GB See all hardware for SOLAR 10.7B v1.0

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-10-7b-v1-0-gguf-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: