TeleChat2-3B 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
Tesla C1080
4 GB · Q6_K · 17.9 tok/s
Fastest card
B200
1,129 tok/s · 180 GB
Which GPUs can run TeleChat2-3B?
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.
818 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
1,129
tok/s
678–1,807 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 3.9 GB | Q8_0 | Comfortable |
|
1,129
tok/s
678–1,807 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 3.9 GB | Q8_0 | Comfortable |
|
902
tok/s
541–1,443 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 3.9 GB | Q8_0 | Comfortable |
|
902
tok/s
541–1,443 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 3.9 GB | Q8_0 | Comfortable |
|
721
tok/s
433–1,154 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 3.9 GB | Q8_0 | Comfortable |
|
690
tok/s
414–1,105 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 3.9 GB | Q8_0 | Comfortable |
|
690
tok/s
414–1,105 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 3.9 GB | Q8_0 | Comfortable |
|
661
tok/s
396–1,057 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 3.9 GB | Q8_0 | Comfortable |
|
586
tok/s
352–938 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 3.9 GB | Q8_0 | Comfortable |
|
586
tok/s
352–938 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 3.9 GB | Q8_0 | Comfortable |
|
586
tok/s
352–938 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 3.9 GB | Q8_0 | Comfortable |
|
556
tok/s
334–890 · low confidence |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 3.9 GB | Q8_0 | Comfortable |
|
474
tok/s
285–759 · low confidence |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 3.9 GB | Q8_0 | Comfortable |
|
474
tok/s
285–759 · low confidence |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 3.9 GB | Q8_0 | Comfortable |
|
474
tok/s
285–759 · low confidence |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 3.9 GB | Q8_0 | Comfortable |
|
474
tok/s
285–759 · low confidence |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 3.9 GB | Q8_0 | Comfortable |
|
474
tok/s
285–759 · low confidence |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 3.9 GB | Q8_0 | Comfortable |
|
361
tok/s
217–578 · low confidence |
Radeon Instinct MI250 AMD | 128 GB | 3,280 GB/s | Nov 2021 | 3.9 GB | Q8_0 | Comfortable |
|
361
tok/s
217–578 · low confidence |
Radeon Instinct MI250X AMD | 128 GB | 3,280 GB/s | Nov 2021 | 3.9 GB | Q8_0 | Comfortable |
|
301
tok/s
181–482 · low confidence |
Data Center GPU Max 1550 Intel | 128 GB | 3,280 GB/s | Jan 2023 | 3.9 GB | Q8_0 | Comfortable |
|
295
tok/s
177–471 · low confidence |
Data Center GPU Max Subsystem Intel | 128 GB | 3,210 GB/s | Jan 2023 | 3.9 GB | Q8_0 | Comfortable |
|
288
tok/s
173–461 · low confidence |
A100 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Nov 2020 | 3.9 GB | Q8_0 | Comfortable |
|
288
tok/s
173–461 · low confidence |
A100X NVIDIA | 80 GB | 2,040 GB/s | Jun 2021 | 3.9 GB | Q8_0 | Comfortable |
|
288
tok/s
173–461 · low confidence |
A800 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Aug 2022 | 3.9 GB | Q8_0 | Comfortable |
|
288
tok/s
173–461 · low confidence |
H100 CNX NVIDIA | 80 GB | 2,040 GB/s | Mar 2023 | 3.9 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
- China Telecom
- Organisation type
- Industry
- Country
- China
- Published
- 8 November 2024
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, Quantitative reasoning, Chat, Text summarization, Code generation
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
- 3B
- 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 (restricted use)
- Training code
- Open (restricted use)
- Hugging Face
- Tele-AI
Apache 2.0 https://huggingface.co/Tele-AI/TeleChat2-3B "Community use TeleChat model needs to follow 《TeleChat model community license agreement》. TeleChat model supports commercial use if you plan to treat TeleChat The model or its derivatives are used for commercial purposes, you need to contact the mailbox below" no clear license but same disclaimer as above https://github.com/Tele-AI/TeleChat2/ this is seems to be pre-training code: https://github.com/Tele-AI/TeleChat2/tree/main/deepspeed
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- TeleChat2
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run TeleChat2-3B
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 B300 288 GB · 8,000 GB/s · Q8_0 1,129 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 1,129 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 902 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 902 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 721 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 690 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 690 tok/s
- 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 661 tok/s
- 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 586 tok/s
- 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 586 tok/s
The smallest GPUs that still run TeleChat2-3B
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 GeForce RTX 4010 4 GB · needs 3.2 GB · Q6_K · tight 19.7 tok/s
- 02 RTX A400 4 GB · needs 3.2 GB · Q6_K · tight 19.7 tok/s
- 03 RTX 500 Mobile Ada Generation 4 GB · needs 3.2 GB · Q6_K · tight 26.3 tok/s
- 04 GeForce RTX 3050 A Mobile 4 GB · needs 3.2 GB · Q6_K · tight 39.4 tok/s
- 05 Jetson Orin Nano 4 GB 4 GB · needs 3.2 GB · Q6_K · tight 7.0 tok/s
- 06 Radeon RX 6450M 4 GB · needs 3.2 GB · Q6_K · tight 20.5 tok/s
- 07 Radeon RX 6550M 4 GB · needs 3.2 GB · Q6_K · tight 23.0 tok/s
- 08 Radeon RX 6550S 4 GB · needs 3.2 GB · Q6_K · tight 20.5 tok/s
- 09 Arc A310 4 GB · needs 3.2 GB · Q6_K · tight 16.5 tok/s
- 10 Arc Pro A30M 4 GB · needs 3.2 GB · Q6_K · tight 17.1 tok/s
What the numbers mean
What you need to run it
Minimum card
Tesla C1080
Memory needed
3.2 GB
Fastest
1,129 tok/s
TeleChat2-3B is small enough at 3B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.
The least hardware that works is a Tesla C1080. Its 4 GB is enough at Q6_K compression, giving roughly 17.9 tokens per second.
Top of the range is the B200, at roughly 1,129 tokens per second thanks to 8,000 GB/s of bandwidth.
What this model is
TeleChat2-3B was published by China Telecom, in China, in November 2024. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling/generation, Question answering, Quantitative reasoning, Chat, Text summarization, Code generation.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the Tele-AI organisation on Hugging Face.
What decides the speed
The median result is around 35.8 tokens per second; 780 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.
Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.
Step by step
How to choose a GPU for TeleChat2-3B
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
Every card here has been checked against TeleChat2-3B — around 3.2 GB at Q6_K. Capacity is the gate — a card either holds it or it does not.
-
02
Set the context length you will work at
Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for TeleChat2-3B.
-
03
Set a quality floor
The quantisation column varies by card, because a bigger card holds a more accurate copy of TeleChat2-3B — Q6_K on the smallest card that fits. Set a floor to hold the comparison at one level.
-
04
Compare tokens per second, not specifications
The speed ordering for TeleChat2-3B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 1,129 tok/s.
-
05
Check the fit verdict before buying
The fit column separates cards that just manage TeleChat2-3B from those with room to spare. Buy for the second if the context might grow.
-
06
Open the card you have settled on
Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for TeleChat2-3B alone — a card is usually bought for more than one model.
Answers
TeleChat2-3B — common questions
Can I run TeleChat2-3B on a 24 GB GPU?
Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.9 GB and generating roughly 189 tokens per second — a comfortable fit.
Is TeleChat2-3B open source?
Its weights are published, so TeleChat2-3B 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 TeleChat2-3B have?
TeleChat2-3B has 3B parameters. 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 TeleChat2-3B?
TeleChat2-3B was published by China Telecom, based in China, categorised as industry.
When was TeleChat2-3B released?
TeleChat2-3B was published in November 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 TeleChat2-3B used for?
TeleChat2-3B works in Language, and is recorded as handling language modeling/generation, Question answering, Quantitative reasoning, Chat, Text summarization, Code generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download TeleChat2-3B?
Its weights are published under the Tele-AI organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
Can I run TeleChat2-3B 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. Our figures for TeleChat2-3B assume it is fully resident.
Would two GPUs run TeleChat2-3B faster?
A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run TeleChat2-3B alone, the case for pairing is weak.
Why does the quantisation differ between cards for TeleChat2-3B?
Because capacity varies, so does how hard TeleChat2-3B has to be squeezed — 2 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these TeleChat2-3B 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 678–1,807 tok/s on the B200 rather than a single number.
What GPU do I need to run TeleChat2-3B?
The smallest card in our catalogue that holds TeleChat2-3B is the Tesla C1080, with 4 GB of memory. It runs the model at Q6_K using about 3.2 GB, and produces roughly 17.9 tokens per second. 818 cards in total can run it.
How fast is TeleChat2-3B on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 1,129 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 780 of the cards that can run TeleChat2-3B clear that.
How much VRAM does TeleChat2-3B need?
About 3.2 GB at Q6_K 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 TeleChat2-3B on a 8 GB GPU?
Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.9 GB and generating roughly 210 tokens per second — a comfortable fit.
Can I run TeleChat2-3B on a 12 GB GPU?
Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.9 GB and generating roughly 129 tokens per second — a comfortable fit.
Can I run TeleChat2-3B on a 16 GB GPU?
Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.9 GB and generating roughly 160 tokens per second — a comfortable fit.
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.