Upstage·Solar·LlamaForCausalLM

SOLAR 10.7B v1.0 — Hardware Requirements & GPU Compatibility

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SOLAR 10.7B v1.0 is a 10.7B-parameter open language model from Upstage in the Solar family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 7.14 GB of VRAM — see which GPUs and Macs can run it below.

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Specifications

Publisher
Upstage
Family
Solar
Parameters
10.7B
Architecture
LlamaForCausalLM
Context Length
4,096 tokens
Vocabulary Size
32,000
Release Date
2023-12-12
License
Apache 2.0

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How Much VRAM Does SOLAR 10.7B v1.0 Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.405.3 GB
Q3_K_Mest.3.905.9 GB
Q4_K_Mest.4.807.1 GB
Q5_K_Mest.5.708.3 GB
Q6_Kest.6.609.6 GB
Q8_0est.8.0011.4 GB
FP16est.16.0022.2 GB

est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.

Which GPUs Can Run SOLAR 10.7B v1.0?

Q4_K_M · 7.1 GB

SOLAR 10.7B v1.0 (Q4_K_M) requires 7.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 10+ GB is recommended. Using the full 4K context window can add up to 0.4 GB, bringing total usage to 7.5 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.

Which Devices Can Run SOLAR 10.7B v1.0?

Q4_K_M · 7.1 GB

33 devices with unified memory can run SOLAR 10.7B v1.0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).

Related Models

Frequently Asked Questions

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

SOLAR 10.7B v1.0 requires 7.1 GB of VRAM at Q4_K_M, or 22.2 GB at FP16.

VRAM = Weights + KV Cache + Overhead

Weights = 10.7B × 4.8 bits ÷ 8 = 6.4 GB

KV Cache + Overhead 0.7 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 1.1 GB (at full 4K context)

VRAM usage by quantization

7.1 GB
7.5 GB

Learn more about VRAM estimation →

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

For SOLAR 10.7B v1.0, Q4_K_M (7.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (8.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 5.3 GB.

VRAM requirement by quantization

Q2_K
5.3 GB
Q4_K_M
7.1 GB
Q5_K_M
8.3 GB
Q6_K
9.6 GB
Q8_0
11.4 GB
FP16
22.2 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run SOLAR 10.7B v1.0 on a Mac?

SOLAR 10.7B v1.0 requires at least 5.3 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run SOLAR 10.7B v1.0 locally?

Yes — SOLAR 10.7B v1.0 can run locally on consumer hardware. At Q4_K_M quantization it needs 7.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is SOLAR 10.7B v1.0?

At Q4_K_M, SOLAR 10.7B v1.0 can reach ~408 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~92 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: AMD Instinct MI300X5300 ÷ 7.1 × 0.55 = ~408 tok/s

Estimated speed at Q4_K_M (7.1 GB)

~408 tok/s
~92 tok/s
~305 tok/s
~252 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of SOLAR 10.7B v1.0?

At Q4_K_M, the download is about 6.44 GB. The full-precision FP16 version is 21.46 GB. The smallest option (Q2_K) is 4.56 GB.

Which GPUs can run SOLAR 10.7B v1.0?

35 consumer GPUs can run SOLAR 10.7B v1.0 at Q4_K_M (7.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 27 GPUs have plenty of headroom for comfortable inference.

Which devices can run SOLAR 10.7B v1.0?

33 devices with unified memory can run SOLAR 10.7B v1.0 at Q4_K_M (7.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.