Can SOLAR 10.7B v1.0 run on RTX 4070 Super 12GB?

YES — Tight Fit

C52Usable

Estimated from fit model

SOLAR 10.7B v1.0 needs ~9.9 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 9.9 GB, 62.4 tok/s, Tight fit

9.9 GB required12.0 GB available

83% VRAM used

Fit status

Tight fit

Decode

62.4 tok/s

TTFT

3101 ms

Safe context

43K

Memory

9.9 GB / 12.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsSOLAR 10.7B v1.0 on RTX 4070 Super 12GB
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: 62.4 tok/s decode · 3.1s TTFT (warm) · 156 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	62.4 tok/s	1691 ms	43K
Coding	C	Tight fit	62.4 tok/s	3101 ms	43K
Agentic Coding	C	Tight fit	62.4 tok/s	4511 ms	43K
Reasoning	C	Tight fit	62.4 tok/s	3665 ms	43K
RAG	C	Tight fit	62.4 tok/s	5638 ms	43K

Quantization options

How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C50
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 5060 Ti 16GBBudget pick

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.41.3 tok/s decode

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

~$449 MSRP

RTX 5070 Ti 16GBBest value

16 GB VRAM (+4)896 GB/s (+392)

Raises estimated decode speed by about 37%.85.2 tok/s decode

Raises estimated decode speed by about 37%.

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

~$749 MSRP

RTX 4070 Ti Super 16GBNVIDIA upgrade

16 GB VRAM (+4)672 GB/s (+168)

Raises estimated decode speed by about 39%.86.5 tok/s decode

Raises estimated decode speed by about 39%.

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

~$799 MSRP

Frequently asked questions

Can RTX 4070 Super 12GB run SOLAR 10.7B v1.0?

Yes, RTX 4070 Super 12GB can run SOLAR 10.7B v1.0 with a C grade (Tight fit). Expected decode speed: 62.4 tok/s.

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

SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 9.9 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 4070 Super 12GB?

On RTX 4070 Super 12GB, SOLAR 10.7B v1.0 achieves approximately 62.4 tokens per second decode speed with a time-to-first-token of 3101ms using Q4_K_M quantization.

Can RTX 4070 Super 12GB run SOLAR 10.7B v1.0 for coding?

For coding workloads, SOLAR 10.7B v1.0 on RTX 4070 Super 12GB receives a C grade with 62.4 tok/s and 43K context.

What context window can SOLAR 10.7B v1.0 use on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, SOLAR 10.7B v1.0 can safely use up to 43K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4070 Super 12GB See all hardware for SOLAR 10.7B v1.0

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