Solar-10.7B (Solar Mini) TPS calculator

Open weights Upstage 10.7B parameters December 2023

Each card below is assessed against this model at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from the card's memory bandwidth and the size of the model once compressed.

Calculated for this model

509 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 5110P

8 GB · Q4_K_M · 19.0 tok/s

Fastest card

B200

317 tok/s · 180 GB

Which GPUs can run Solar-10.7B (Solar Mini)?

Set the inputs, read the answer

Context length tokens

A longer conversation needs more memory, which can push this model off smaller cards.

Minimum quality

Hides cards that would only fit the model by compressing it below this point.

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Card memory

Also show cards that cannot run it

509 cards match

Calculating

					Needs	Quantisation	Fit
317 tok/s 190–507 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	12.2 GB	Q8_0	Comfortable
317 tok/s 190–507 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	12.2 GB	Q8_0	Comfortable
253 tok/s 152–405 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	12.2 GB	Q8_0	Comfortable
253 tok/s 152–405 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	12.2 GB	Q8_0	Comfortable
202 tok/s 121–324 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	12.2 GB	Q8_0	Comfortable
194 tok/s 116–310 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	12.2 GB	Q8_0	Comfortable
194 tok/s 116–310 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	12.2 GB	Q8_0	Comfortable
185 tok/s 111–296 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	12.2 GB	Q8_0	Comfortable
164 tok/s 99–263 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	12.2 GB	Q8_0	Comfortable
164 tok/s 99–263 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	12.2 GB	Q8_0	Comfortable
164 tok/s 99–263 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	12.2 GB	Q8_0	Comfortable
156 tok/s 94–250 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	12.2 GB	Q8_0	Comfortable
136 tok/s 82–218 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	7.2 GB	Q4_K_M	Tight
133 tok/s 80–213 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	12.2 GB	Q8_0	Comfortable
133 tok/s 80–213 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	12.2 GB	Q8_0	Comfortable
133 tok/s 80–213 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	12.2 GB	Q8_0	Comfortable
133 tok/s 80–213 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	12.2 GB	Q8_0	Comfortable
133 tok/s 80–213 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	12.2 GB	Q8_0	Comfortable
110 tok/s 66–176 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.4 GB	Q5_K_M	Tight
101 tok/s 61–162 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	12.2 GB	Q8_0	Comfortable
101 tok/s 61–162 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	12.2 GB	Q8_0	Comfortable
84.4 tok/s 51–135 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	12.2 GB	Q8_0	Comfortable
82.6 tok/s 50–132 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	12.2 GB	Q8_0	Comfortable
80.8 tok/s 48–129 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	12.2 GB	Q8_0	Comfortable
80.8 tok/s 48–129 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	12.2 GB	Q8_0	Comfortable

Speeds are estimates for a single request — one conversation at a time — calculated from memory bandwidth, model size and quantisation. Real throughput varies with the inference runtime and its version. Figures published by hardware vendors measure many simultaneous requests and are much higher.

On record

Full specification

Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.

Origin

Who built this model, where, and when it was published.

Organisation: Upstage
Organisation type: Industry
Country: Korea (Republic of)
Published: 23 December 2023
Authors: Dahyun Kim, Chanjun Park, Sanghoon Kim, Wonsung Lee, Wonho Song, Yunsu Kim, Hyeonwoo Kim, Yungi Kim, Hyeonju Lee, Jihoo Kim, Changbae Ahn, Seonghoon Yang, Sukyung Lee, Hyunbyung Park, Gyoungjin Gim, Mikyoung Cha, Hwalsuk Lee, Sunghun Kim

What it does

The problem areas the model was built for. A model can carry several of each.

Domain: Language
Task: Chat, Language modeling/generation, Question answering
Base model: Mistral 7B

Size

How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.

Parameters: 10.7B
Training data: tokens

Availability

Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.

Weights: Open — downloadable
Model access: Open weights (unrestricted)
Training code: Unreleased
Hugging Face: upstage

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Record confidence: Likely
Citations: 216

Sources

Where this record came from and when it was last checked.

Reference: SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Solar-10.7B (Solar Mini)

Ranked by estimated tokens per second, newest card first where speeds tie. Because generation is bound by memory bandwidth, this ordering follows bandwidth rather than any gaming benchmark.

The smallest GPUs that still run Solar-10.7B (Solar Mini)

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

What the numbers mean

Hardware requirements in practice

Minimum card

Xeon Phi 5110P

Memory needed

7.2 GB

Fastest

317 tok/s

Solar-10.7B (Solar Mini) is small enough at 10.7B parameters that hardware is rarely the obstacle — 509 of the cards we track can run it, including cards several years old.

The entry point is the Xeon Phi 5110P: 8 GB of memory, Q4_K_M compression, roughly 19.0 tokens per second.

At the other end, a B200 generates roughly 317 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Background

Solar-10.7B (Solar Mini) was published by Upstage, in Korea (Republic of), in December 2023. industry is the category the publisher falls under.

It works in Language, and is recorded as doing chat, Language modeling/generation, Question answering.

It is derived from Mistral 7B rather than trained from scratch, which is the usual way a specialised model is produced.

The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold. It is published under the upstage organisation on Hugging Face.

Reading the throughput figures

Half the cards that hold it manage more than 20.5 tokens per second, and 461 exceed reading speed outright.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.

Step by step

How to choose a GPU for Solar-10.7B (Solar Mini)

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Read the memory figure first

Look at what Solar-10.7B (Solar Mini) actually needs — around 7.2 GB at Q4_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Match the context to your actual use

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Solar-10.7B (Solar Mini).
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold — Q4_K_M on the smallest card that fits. Setting a floor drops the cards that only manage Solar-10.7B (Solar Mini) by squeezing it further than you would want.
04

Compare tokens per second, not specifications

Ranking by tokens per second for Solar-10.7B (Solar Mini) follows memory bandwidth, not core counts, which is why the B200 tops it at 317 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage Solar-10.7B (Solar Mini) from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Following a card through to its own page shows every other model it can hold, which is the question that follows once Solar-10.7B (Solar Mini) is settled.

Answers

Solar-10.7B (Solar Mini) — common questions

What GPU do I need to run Solar-10.7B (Solar Mini)?

The smallest card in our catalogue that holds Solar-10.7B (Solar Mini) is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at Q4_K_M using about 7.2 GB, and produces roughly 19.0 tokens per second. 509 cards in total can run it.

How fast is Solar-10.7B (Solar Mini) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 317 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 461 of the cards that can run Solar-10.7B (Solar Mini) clear that.

How much VRAM does Solar-10.7B (Solar Mini) need?

About 7.2 GB at Q4_K_M compression, which is what the smallest card that runs it uses. Less compression needs more: the figures in the memory column above are recalculated for each card, because each one holds the least-compressed version it can.

Can I run Solar-10.7B (Solar Mini) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q4_K_M, using about 7.2 GB and generating roughly 136 tokens per second — a tight fit.

Can I run Solar-10.7B (Solar Mini) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q6_K, using about 9.7 GB and generating roughly 52.5 tokens per second — a tight fit.

Can I run Solar-10.7B (Solar Mini) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 12.2 GB and generating roughly 44.7 tokens per second — a tight fit.

Can I run Solar-10.7B (Solar Mini) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 12.2 GB and generating roughly 53.0 tokens per second — a comfortable fit.

Is Solar-10.7B (Solar Mini) open source?

Its weights are published, so Solar-10.7B (Solar Mini) can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.

How many parameters does Solar-10.7B (Solar Mini) have?

Solar-10.7B (Solar Mini) has 10.7B parameters. 10.7B. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.

Who created Solar-10.7B (Solar Mini)?

Solar-10.7B (Solar Mini) was published by Upstage, based in Korea (Republic of), categorised as industry.

When was Solar-10.7B (Solar Mini) released?

Solar-10.7B (Solar Mini) was published in December 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

What is Solar-10.7B (Solar Mini) used for?

Solar-10.7B (Solar Mini) works in Language, and is recorded as handling chat, Language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Solar-10.7B (Solar Mini)?

Its weights are published under the upstage organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

Can I run Solar-10.7B (Solar Mini) if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down — the nearest miss we calculate is short by 1.8 GB. Our figures for Solar-10.7B (Solar Mini) assume it is fully resident.

Would two GPUs run Solar-10.7B (Solar Mini) faster?

Two cards buy memory rather than speed. That matters for Solar-10.7B (Solar Mini) only if one card cannot hold it — 509 can, so a second adds little.

Why does the quantisation differ between cards for Solar-10.7B (Solar Mini)?

Because capacity varies, so does how hard Solar-10.7B (Solar Mini) has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Solar-10.7B (Solar Mini) speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 190–507 tok/s on the B200 rather than a single number.

Source

Original publication

Record last updated 25 May 2026

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