Code Llama-34B TPS calculator

Open weights Meta AI 34B parameters August 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

132 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX A4500

20 GB · Q3_K_M · 21.5 tok/s

Fastest card

B200

99.7 tok/s · 180 GB

Which GPUs can run Code Llama-34B?

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

132 cards match

Calculating

					Needs	Quantisation	Fit
99.7 tok/s 85–120	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	36.5 GB	Q8_0	Comfortable
99.7 tok/s 85–120	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	36.5 GB	Q8_0	Comfortable
79.6 tok/s 48–127 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	36.5 GB	Q8_0	Comfortable
79.6 tok/s 48–127 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	36.5 GB	Q8_0	Comfortable
63.6 tok/s 38–102 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	36.5 GB	Q8_0	Comfortable
60.9 tok/s 52–73	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	36.5 GB	Q8_0	Comfortable
60.9 tok/s 52–73	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	36.5 GB	Q8_0	Comfortable
58.3 tok/s 35–93 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	36.5 GB	Q8_0	Comfortable
51.7 tok/s 31–83 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	36.5 GB	Q8_0	Comfortable
51.7 tok/s 31–83 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	36.5 GB	Q8_0	Comfortable
51.7 tok/s 31–83 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	36.5 GB	Q8_0	Comfortable
49.1 tok/s 42–59	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	36.5 GB	Q8_0	Comfortable
41.9 tok/s 36–50	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	36.5 GB	Q8_0	Comfortable
41.9 tok/s 36–50	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	36.5 GB	Q8_0	Comfortable
41.9 tok/s 36–50	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	36.5 GB	Q8_0	Comfortable
41.9 tok/s 36–50	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	36.5 GB	Q8_0	Comfortable
41.9 tok/s 36–50	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	36.5 GB	Q8_0	Comfortable
38.5 tok/s 33–46	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	20.6 GB	Q4_K_M	Tight
35.1 tok/s 30–42	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	20.6 GB	Q4_K_M	Tight
33.9 tok/s 29–41	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	28.6 GB	Q6_K	Tight
33.9 tok/s 29–41	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	28.6 GB	Q6_K	Tight
32.4 tok/s 28–39	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	28.6 GB	Q6_K	Tight
32.4 tok/s 28–39	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	28.6 GB	Q6_K	Tight
31.9 tok/s 19–51 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	36.5 GB	Q8_0	Comfortable
31.9 tok/s 19–51 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	36.5 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: Meta AI
Organisation type: Industry
Country: United States of America
Published: 14 August 2023
Authors: Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Ellen Tan, Yossef (Yossi) Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Defossez, Jade Copet, Faisal Azhar, Hugo Touvron, Gabriel Synnaeve, Louis Martin, Nicolas Usunier, Thomas Scialom

What it does

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

Domain: Language
Task: Code generation
Base model: Llama 2-34B

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: 34B
Training data: 600,000,000,000 tokens
Batch size: 4,000,000

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: 5.3 × 10²³ FLOP
How it was established: Operation counting
Fine-tuning compute: 1.2 × 10²³ FLOP

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 A100 SXM4 80 GB

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: Unreleased

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
Citations: 3,163

Sources

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

Reference: Code Llama: Open Foundation Models for Code
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Code Llama-34B

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 Code Llama-34B

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

RTX A4500

Memory needed

16.7 GB

Fastest

99.7 tok/s

With 34B parameters, Code Llama-34B lands in the range a serious desktop card can handle once the weights are compressed. 132 of the cards we track can run it.

The smallest card that holds it is the RTX A4500 with 20 GB, running it at Q3_K_M and producing around 21.5 tokens per second.

Top of the range is the B200, at roughly 99.7 tokens per second thanks to 8,000 GB/s of bandwidth.

What this model is

Code Llama-34B was published by Meta AI, in United States of America, in August 2023. industry is the category the publisher falls under.

It works in Language, and is recorded as doing code generation.

Its starting point was Llama 2-34B — most models at this scale are adapted from an existing base rather than built from nothing.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

What decides the speed

Half the cards that hold it manage more than 19.7 tokens per second, and 100 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.

Because the architecture is recorded, the memory column is derived rather than estimated.

What went into building it

Training it took roughly 5.3 × 10²³ FLOP of computation, on NVIDIA A100 SXM4 80 GB — a measure of what producing the model cost, not of how fast it answers.

Around 600,000,000,000 tokens went into training it.

Step by step

How to choose a GPU for Code Llama-34B

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 Code Llama-34B — around 16.7 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
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 Code Llama-34B.
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy of Code Llama-34B — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Sort by speed

Sort by speed to see how cards rank for Code Llama-34B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 99.7 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage Code Llama-34B from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Code Llama-34B.

Answers

Code Llama-34B — common questions

How much VRAM does Code Llama-34B need?

About 16.7 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.

Can I run Code Llama-34B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q4_K_M, using about 20.6 GB and generating roughly 38.5 tokens per second — a tight fit.

Is Code Llama-34B open source?

Its weights are published, so Code Llama-34B 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 Code Llama-34B have?

Code Llama-34B has 34B parameters. 34B. 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 Code Llama-34B?

Code Llama-34B was published by Meta AI, based in United States of America, categorised as industry.

When was Code Llama-34B released?

Code Llama-34B was published in August 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 Code Llama-34B used for?

Code Llama-34B works in Language, and is recorded as handling code generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Code Llama-34B?

The weights for Code Llama-34B 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 Code Llama-34B?

Around 5.3 × 10²³ FLOP, on NVIDIA A100 SXM4 80 GB. 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 Code Llama-34B 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 6.2 GB. Our figures for Code Llama-34B assume it is fully resident.

Would two GPUs run Code Llama-34B faster?

A second card roughly doubles the memory available but not the generation rate. With 132 cards already able to run Code Llama-34B alone, the case for pairing is weak.

Why does the quantisation differ between cards for Code Llama-34B?

Because capacity varies, so does how hard Code Llama-34B has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Code Llama-34B speed estimates?

These are estimates with real error bars. The fastest result here, 85–120 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run Code Llama-34B?

The smallest card in our catalogue that holds Code Llama-34B is the RTX A4500, with 20 GB of memory. It runs the model at Q3_K_M using about 16.7 GB, and produces roughly 21.5 tokens per second. 132 cards in total can run it.

How fast is Code Llama-34B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 99.7 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 100 of the cards that can run Code Llama-34B clear that.

Source

Original publication

Record last updated 25 May 2026

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