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.

Search cards

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) reaches a parameter count of 10.7B. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 509.

The entry point is Xeon Phi 5110P, with a memory capacity of 8 GB, running it at a compression of Q4_K_M and producing around 19.0 tokens per second.

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

Background

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

It works in the domain of Language, and is recorded as performing the task of chat, Language modeling/generation, Question answering.

Rather than being trained from scratch, it is derived from Mistral 7B. That 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. On Hugging Face it is published under the organisation upstage.

Reading the throughput figures

Half the cards that hold it manage more than 20.5 tokens per second. Producing text faster than most people read it: 461 of them.

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

Start from what it actually needs, which is the requirement of Solar-10.7B (Solar Mini), needing around 7.2 GB at a compression of Q4_K_M. That figure, not the headline performance of a card, is what decides whether it runs.
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, reaching a compression of Q4_K_M on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Compare tokens per second, not specifications

Ranking by tokens per second follows memory bandwidth rather than core counts, for Solar-10.7B (Solar Mini). It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 317 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage it from those with room to spare, in the case of Solar-10.7B (Solar Mini). Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
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 you have settled on Solar-10.7B (Solar Mini).

Answers

Solar-10.7B (Solar Mini) — common questions

Solar-10.7B (Solar Mini)— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Xeon Phi 5110P, with a memory capacity of 8 GB. It runs the model at a compression of Q4_K_M using about 7.2 GB, and produces roughly 19.0 tokens per second. The number of cards able to run it in total: 509.

Solar-10.7B (Solar Mini)— how fast is it on a GPU?

It depends on the card. The quickest we calculate is 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 the number of cards clearing that: 461.

Solar-10.7B (Solar Mini)— how much VRAM does it need?

It needs about 7.2 GB at a compression of Q4_K_M, 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.

Solar-10.7B (Solar Mini)— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q4_K_M, using about 7.2 GB and generating roughly 136 tokens per second. The fit is tight.

Solar-10.7B (Solar Mini)— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q6_K, using about 9.7 GB and generating roughly 52.5 tokens per second. The fit is tight.

Solar-10.7B (Solar Mini)— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 12.2 GB and generating roughly 44.7 tokens per second. The fit is tight.

Solar-10.7B (Solar Mini)— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 12.2 GB and generating roughly 53.0 tokens per second. The fit is comfortable.

Solar-10.7B (Solar Mini)— is it open source?

Its weights are published, so it 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.

Solar-10.7B (Solar Mini)— how many parameters does it have?

It has a parameter count of 10.7B. 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.

Solar-10.7B (Solar Mini)— who created it?

It was published by Upstage, based in Korea (Republic of), an organisation categorised as industry.

Solar-10.7B (Solar Mini)— when was it released?

It 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.

Solar-10.7B (Solar Mini)— what is it used for?

It works in the domain of Language, and is recorded as handling the task of chat, Language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Solar-10.7B (Solar Mini)— where can I download it?

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

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

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. The nearest miss we calculate falls short by 1.8 GB. Every figure here assumes the whole model is resident on the card.

Solar-10.7B (Solar Mini)— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 509. So a second card is rarely the answer here.

Solar-10.7B (Solar Mini)— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

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

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 190–507 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Source

Original publication

Record last updated 25 May 2026

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