Grok-1 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
Smallest card that fits
B200
180 GB · Q3_K_M · 29.1 tok/s
Fastest card
B200
29.1 tok/s · 180 GB
Which GPUs can run Grok-1?
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.
7 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
29.1
tok/s
17–47 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 154.1 GB | Q3_K_M | Tight |
|
19.3
tok/s
12–31 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 227.2 GB | Q5_K_M | Tight |
|
15.4
tok/s
9–25 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 227.2 GB | Q5_K_M | Tight |
|
15.4
tok/s
9–25 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 227.2 GB | Q5_K_M | Tight |
|
13.8
tok/s
8–22 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 172.4 GB | IQ4_XS | Tight |
|
13.8
tok/s
8–22 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 172.4 GB | IQ4_XS | Tight |
|
11.3
tok/s
7–18 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 227.2 GB | Q5_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
- xAI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 4 November 2023
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Chat
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
- 314B
- Training data
- 6,200,000,000,000 tokens
"314B parameter Mixture-of-Experts model with 25% of the weights active on a given token". So effectively 78B parameters Mixture of 8 experts: https://github.com/xai-org/grok-1
(Speculative confidence, see compute notes)
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
- 2.9 × 10²⁴ FLOP
- How it was established
- Benchmarks
- Plausible range
- 2 × 10²⁴ – 7 × 10²⁴ FLOP
"On these benchmarks, Grok-1 displayed strong results, surpassing all other models in its compute class, including ChatGPT-3.5 and Inflection-1. It is only surpassed by models that were trained with a significantly larger amount of training data and compute resources like GPT-4" Per table, Grok-1 is surpassed by Palm 2, Claude 2, GPT-4, so it required less compute than these three models. Palm 2 was trained on 7e24 FLOP. GPT-3.5 is ~2.6e24. Inflection-1's compute is not public/known by us but …
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
apache 2.0 https://github.com/xai-org/grok-1
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Foundation model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Training cost
- Record confidence
- Likely
"On these benchmarks, Grok-1 displayed strong results, surpassing all other models in its compute class, including ChatGPT-3.5 and Inflection-1"
Sources
Where this record came from and when it was last checked.
- Reference
- Announcing Grok
- Last updated
- 18 December 2025
The extremes
The ten fastest GPUs for Grok-1
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.
- 01 B200 180 GB · 8,000 GB/s · Q3_K_M 29.1 tok/s
- 02 B300 288 GB · 8,000 GB/s · Q5_K_M 19.3 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q5_K_M 15.4 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q5_K_M 15.4 tok/s
- 05 Radeon Instinct MI300X 192 GB · 5,325 GB/s · IQ4_XS 13.8 tok/s
- 06 Radeon Instinct MI308X 192 GB · 5,325 GB/s · IQ4_XS 13.8 tok/s
- 07 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q5_K_M 11.3 tok/s
The smallest GPUs that still run Grok-1
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 B200 180 GB · needs 154.1 GB · Q3_K_M · tight 29.1 tok/s
- 02 Radeon Instinct MI300X 192 GB · needs 172.4 GB · IQ4_XS · tight 13.8 tok/s
- 03 Radeon Instinct MI308X 192 GB · needs 172.4 GB · IQ4_XS · tight 13.8 tok/s
- 04 Radeon Instinct MI325X 256 GB · needs 227.2 GB · Q5_K_M · tight 11.3 tok/s
- 05 B300 288 GB · needs 227.2 GB · Q5_K_M · tight 19.3 tok/s
- 06 Radeon Instinct MI350X 288 GB · needs 227.2 GB · Q5_K_M · tight 15.4 tok/s
- 07 Radeon Instinct MI355X 288 GB · needs 227.2 GB · Q5_K_M · tight 15.4 tok/s
What the numbers mean
What it takes to run this model
Minimum card
B200
Memory needed
154.1 GB
Fastest
29.1 tok/s
At 314B parameters, Grok-1 is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job — 7 of the cards we track can hold it on their own, and all of them are datacentre parts.
The smallest card that holds it is the B200 with 180 GB, running it at Q3_K_M and producing around 29.1 tokens per second.
At the other end, a B200 generates roughly 29.1 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.
About this model
Grok-1 was published by xAI, in United States of America, in November 2023. industry is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling, Chat.
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.
How fast it runs, and why
The median result is around 15.4 tokens per second; 7 cards produce text faster than most people read it.
It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.
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.
What went into building it
The training run consumed about 2.9 × 10²⁴ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The training set ran to roughly 6,200,000,000,000 tokens.
Its inclusion criterion is training cost.
Step by step
How to choose a GPU for Grok-1
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Check what it needs before anything else
Look at what Grok-1 actually needs — around 154.1 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
-
02
Match the context to your actual use
Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason Grok-1 stops fitting a card that seemed fine.
-
03
Choose how far you will compress it
Compression is what makes Grok-1 fit smaller cards, at some cost in accuracy — Q3_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
-
04
Compare tokens per second, not specifications
Sort by speed to see how cards rank for Grok-1. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 29.1 tok/s.
-
05
Look at the headroom, not just the fit
A tight fit runs Grok-1 but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
-
06
Check the card from the other side
Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for Grok-1 alone — a card is usually bought for more than one model.
Answers
Grok-1 — common questions
Why does the quantisation differ between cards for Grok-1?
Because capacity varies, so does how hard Grok-1 has to be squeezed — 3 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these Grok-1 speed estimates?
They are calculated from specifications rather than measured, and each carries a range — 17–47 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
What GPU do I need to run Grok-1?
The smallest card in our catalogue that holds Grok-1 is the B200, with 180 GB of memory. It runs the model at Q3_K_M using about 154.1 GB, and produces roughly 29.1 tokens per second. 7 cards in total can run it.
How fast is Grok-1 on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 29.1 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 7 of the cards that can run Grok-1 clear that.
How much VRAM does Grok-1 need?
About 154.1 GB at Q3_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.
Is Grok-1 open source?
Its weights are published, so Grok-1 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 Grok-1 have?
Grok-1 has 314B parameters. "314B parameter Mixture-of-Experts model with 25% of the weights active on a given token". So effectively 78B parameters Mixture of 8 experts: https://github.com/xai-org/grok-1. 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 Grok-1?
Grok-1 was published by xAI, based in United States of America, categorised as industry.
When was Grok-1 released?
Grok-1 was published in November 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 Grok-1 used for?
Grok-1 works in Language, and is recorded as handling language modeling, Chat. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download Grok-1?
The weights for Grok-1 are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
How much compute was used to train Grok-1?
Around 2.9 × 10²⁴ 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.
Can I run Grok-1 if it does not fit in my GPU?
It can be split between the card and system memory, but Grok-1 generates painfully slowly that way — the nearest miss we calculate is short by 63.8 GB. Nothing on this page assumes offloading.
Would two GPUs run Grok-1 faster?
Two cards buy memory rather than speed. That matters for Grok-1 only if one card cannot hold it — 7 can, so a second adds little.
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.