Llama 3.1-405B 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
Radeon Instinct MI325X
256 GB · Q4_K_M · 11.3 tok/s
Fastest card
B300
19.3 tok/s · 288 GB
Which GPUs can run Llama 3.1-405B?
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.
4 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
19.3
tok/s
16–23 |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 224.3 GB | Q4_K_M | Tight |
|
15.4
tok/s
9–25 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 224.3 GB | Q4_K_M | Tight |
|
15.4
tok/s
9–25 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 224.3 GB | Q4_K_M | Tight |
|
11.3
tok/s
7–18 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 224.3 GB | Q4_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
- Meta AI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 23 July 2024
- Authors
- Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Roziere, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Ch…
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, Mathematical reasoning
- Numerical format
- BF16
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
- 405B
- Training data
- 15,600,000,000,000 tokens
- Epochs
- 1
- Batch size
- 16,000,000
405B
15.6T tokens
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 × 10²⁵ FLOP
- How it was established
- Reported,Operation counting
- Post-training compute
- 9.4 × 10²² FLOP
Stated in paper. Also, 6 * 405B * 15.6T training tokens = 3.8e25
Section 4 gives detail about the post-training process. They do 6 rounds of post-training, using the model from the previous iteration in each successive round. In each round, they fine-tune a copy of the language model into a reward model (RM) using preference data, then use the reward model to do rejection sampling on human annotation prompts. Next they do supervised fine-tuning (SFT) on the rejection sampled data along with some synthetic data (8.5k to 9k steps per round). Next, they do Direc…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA H100 SXM5 80GB
- Chips used
- 16,384
- Wall-clock time
- 2,142 hours (89.3 days)
- Hardware utilisation
- MFU 40.4%
- Power draw
- 22.6 MW
- Compute cost
- $52,885,434
Trained on 30.84M GPU hours (https://huggingface.co/blog/llama31) and used "up to 16K H100 GPU[s]" so training took at least 30.84M / 16k = 1927.5 hours or ~80 days. Section 3.3.4 gives reliability details over a 54 day period during training, for which they had "higher than 90% effective training time" 1927.5 / 0.9 = 2142 hours Probably, full training time is somewhat longer, since it sounds like there were periods where not all 16k H100s were running.
MFU ranges between 0.38 and 0.43 depending on the specific parallelism used; I assume the geometric mean: sqrt(0.38 * 0.43) = 0.4042
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 (restricted use)
- Training code
- Open (restricted use)
- Hugging Face
- meta-llama
Llama 3.1 model license: https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE must seek separate license if over 700m monthly users, acceptable use restrictions training code here: https://github.com/meta-llama/llama-recipes/blob/main/src/llama_recipes/utils/train_utils.py#L70
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- SOTA improvement,Training cost
- Record confidence
- Confident
High training compute, exceeds 4o and Claude 3.5 on some benchmarks: https://ai.meta.com/blog/meta-llama-3-1/
Sources
Where this record came from and when it was last checked.
- Reference
- The Llama 3 Herd of Models
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run Llama 3.1-405B
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 Llama 3.1-405B
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
The hardware side
Minimum card
Radeon Instinct MI325X
Memory needed
224.3 GB
Fastest
19.3 tok/s
At 405B parameters, Llama 3.1-405B is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job — 4 of the cards we track can hold it on their own, and all of them are datacentre parts.
At the low end, a Radeon Instinct MI325X handles it — 256 GB, at Q4_K_M, for about 11.3 tokens per second.
At the other end, a B300 generates roughly 19.3 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.
About this model
Llama 3.1-405B was published by Meta AI, in United States of America, in July 2024. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling/generation, Question answering, Code generation, Mathematical reasoning.
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 meta-llama organisation on Hugging Face.
How fast it runs, and why
The median result is around 15.4 tokens per second; 4 cards produce text faster than most people read it.
Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.
Its attention layout is on file, so the memory figures are computed exactly rather than approximated.
Training and provenance
Training it took roughly 3.8 × 10²⁵ FLOP of computation, on NVIDIA H100 SXM5 80GB — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 15,600,000,000,000 tokens of text.
Its inclusion criterion is sOTA improvement,Training cost.
Step by step
How to choose a GPU for Llama 3.1-405B
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
Every card here has been checked against Llama 3.1-405B — around 224.3 GB at Q4_K_M. Capacity is the gate — a card either holds it or it does not.
-
02
Set the context length you will work at
The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Llama 3.1-405B can slip off a card that handles short questions easily.
-
03
Set a quality floor
Compression is what makes Llama 3.1-405B fit smaller cards, at some cost in accuracy — Q4_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
Ranking by tokens per second for Llama 3.1-405B follows memory bandwidth, not core counts, which is why the B300 tops it at 19.3 tok/s.
-
05
Read the fit column last
Tight means Llama 3.1-405B loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
-
06
Check the card from the other side
Following a card through to its own page shows every other model it can hold, which is the question that follows once Llama 3.1-405B is settled.
Answers
Llama 3.1-405B — common questions
Would two GPUs run Llama 3.1-405B faster?
A second card roughly doubles the memory available but not the generation rate. With 4 cards already able to run Llama 3.1-405B alone, the case for pairing is weak.
Why does the quantisation differ between cards for Llama 3.1-405B?
A larger card holds a more accurate copy. Across the cards that run Llama 3.1-405B, 1 compression levels are used; the floor control above pins it to one.
How accurate are these Llama 3.1-405B 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 16–23 tok/s on the B300 rather than a single number.
What GPU do I need to run Llama 3.1-405B?
The smallest card in our catalogue that holds Llama 3.1-405B is the Radeon Instinct MI325X, with 256 GB of memory. It runs the model at Q4_K_M using about 224.3 GB, and produces roughly 11.3 tokens per second. 4 cards in total can run it.
How fast is Llama 3.1-405B on a GPU?
It depends on the card. The quickest we calculate is a B300 at about 19.3 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 4 of the cards that can run Llama 3.1-405B clear that.
How much VRAM does Llama 3.1-405B need?
About 224.3 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.
Is Llama 3.1-405B open source?
Its weights are published, so Llama 3.1-405B 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 Llama 3.1-405B have?
Llama 3.1-405B has 405B parameters. 405B. 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 Llama 3.1-405B?
Llama 3.1-405B was published by Meta AI, based in United States of America, categorised as industry.
When was Llama 3.1-405B released?
Llama 3.1-405B was published in July 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.
What is Llama 3.1-405B used for?
Llama 3.1-405B works in Language, and is recorded as handling language modeling/generation, Question answering, Code generation, Mathematical reasoning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download Llama 3.1-405B?
Its weights are published under the meta-llama organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
How much compute was used to train Llama 3.1-405B?
Around 3.8 × 10²⁵ FLOP, on NVIDIA H100 SXM5 80GB. 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 Llama 3.1-405B if it does not fit in my GPU?
It can be split between the card and system memory, but Llama 3.1-405B generates painfully slowly that way — the nearest miss we calculate is short by 51.5 GB. Nothing on this page assumes offloading.
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.