Arctic TPS calculator
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
818 cards we hold specifications for
Smallest card that fits
B300
288 GB · Q3_K_M · 19.1 tok/s
Fastest card
B300
19.1 tok/s · 288 GB
Which GPUs can run Arctic?
Set the inputs, read the answer
A longer conversation needs more memory, which can push this model off smaller cards.
Hides cards that would only fit the model by compressing it below this point.
3 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
19.1
tok/s
11–30 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 235.2 GB | Q3_K_M | Tight |
|
15.2
tok/s
9–24 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 235.2 GB | Q3_K_M | Tight |
|
15.2
tok/s
9–24 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 235.2 GB | Q3_K_M | Tight |
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
- Snowflake
- Organisation type
- Industry
- Country
- United States of America
- Published
- 24 April 2024
- Authors
- Snowflake AI Research
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering, Code generation, Quantitative reasoning
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
- 480B
- Training data
- tokens
" It combines a 10B dense transformer model with a residual 128x3.66B MoE MLP resulting in 480B total and 17B active parameters chosen using a top-2 gating."
"Arctic was trained with a three-stage curriculum each with a different data composition focusing on generic skills in the first phase (1T Tokens), and enterprise-focused skills in the latter two phases (1.5T and 1T tokens). " 1+1.5+1 = 3.5
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 3.8 × 10²³ FLOP
- How it was established
- Other,Operation counting
from the graph: 1x - Arctic 1.9X - Llama 3 8B (7.2×10^23) ~ = Yi 34B (6.1e23) -> x = 3.2105263e+23 3X - Code Llama 70B (1.26e+24) -> x = 4.2e+23 17.5X - Llama 3 70B (7.861e24) -> x =4.492e+23 = 3.7975893e+23 Operation counting (17B active parameters): 6ND = 6 FLOP / parameter / token * 17*10^9 parameters * 3.5*10^12 tokens = 3.57e+23 FLOP geometric mean:(3.2105263e+23*4.492e+23*4.2e+23*3.57e+23)^(1/4) = 3.8347175e+23
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chip-hours
- 504,000
- Compute cost
- $2,000,000
- Cloud vendor
- AWS
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
- Open source
Apache 2.0 license with ungated access to weights and code paired with open data recipe and research insights.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Likely above 10²³ FLOP
- Yes
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Snowflake Arctic: The Best LLM for Enterprise AI — Efficiently Intelligent, Truly Open
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run Arctic
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 Arctic
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
What it takes to run this model
Minimum card
B300
Memory needed
235.2 GB
Fastest
19.1 tok/s
Arctic reaches a parameter count of 480B. That is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job, and every card able to hold it alone is a datacentre part. The number that can: 3.
The smallest card that holds it is B300, with a memory capacity of 288 GB, running it at a compression of Q3_K_M and producing around 19.1 tokens per second.
Top of the range is B300, generating roughly 19.1 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.
What this model is
Arctic was published by Snowflake, in the country recorded as United States of America, during April 2024. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Question answering, Code generation, Quantitative reasoning.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
What decides the speed
Half the cards that hold it manage more than 15.2 tokens per second. Exceeding reading speed outright: 3 of them.
Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.
Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.
Training and provenance
Producing it required arithmetic totalling around 3.8 × 10²³ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Step by step
How to choose a GPU for Arctic
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Start from the memory column
The table lists every card able to hold Arctic, needing around 235.2 GB at a compression of Q3_K_M. That figure, not the headline performance of a card, is what decides whether it runs.
-
02
Set the context length you will work at
Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason a card that seemed fine stops fitting Arctic.
-
03
Choose how far you will compress it
Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of Q3_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
The speed ordering is effectively an ordering by memory bandwidth, for Arctic. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B300, at 19.1 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 Arctic. 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
Check the card from the other side
Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond Arctic.
Answers
Arctic — common questions
Arctic— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Question answering, Code generation, Quantitative reasoning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Arctic— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
Arctic— how much compute was used to train it?
Training consumed around 3.8 × 10²³ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Arctic— can I run it if it does not fit in my GPU?
It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 60.7 GB. Every figure here assumes the whole model is resident on the card.
Arctic— 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: 3. So a second card is rarely the answer here.
Arctic— why does the quantisation differ between cards?
Each card is shown running the least-compressed copy it can hold. The number of distinct compression levels across the cards that fit it: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.
Arctic— how accurate are these speed estimates?
They are calculated from specifications rather than measured, and each carries a range. One example: 11–30 tok/s on B300. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
Arctic— what GPU do I need to run it?
The smallest card in our catalogue that holds it is B300, with a memory capacity of 288 GB. It runs the model at a compression of Q3_K_M using about 235.2 GB, and produces roughly 19.1 tokens per second. The number of cards able to run it in total: 3.
Arctic— how fast is it on a GPU?
It depends on the card. The quickest we calculate is B300, at about 19.1 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: 3.
Arctic— how much VRAM does it need?
It needs about 235.2 GB at a compression of Q3_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.
Arctic— 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.
Arctic— how many parameters does it have?
It has a parameter count of 480B. " It combines a 10B dense transformer model with a residual 128x3.66B MoE MLP resulting in 480B total and 17B active parameters chosen using a top-2 gating.". 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.
Arctic— who created it?
It was published by Snowflake, based in United States of America, an organisation categorised as industry.
Arctic— when was it released?
It was published in April 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.