TigerBot-70B 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
A100 PCIe 40 GB
40 GB · Q3_K_M · 25.5 tok/s
Fastest card
B200
48.4 tok/s · 180 GB
Which GPUs can run TigerBot-70B?
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.
61 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
48.4
tok/s
29–77 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 75.6 GB | Q8_0 | Comfortable |
|
48.4
tok/s
29–77 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 75.6 GB | Q8_0 | Comfortable |
|
38.7
tok/s
23–62 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 75.6 GB | Q8_0 | Comfortable |
|
38.7
tok/s
23–62 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 75.6 GB | Q8_0 | Comfortable |
|
30.9
tok/s
19–49 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 75.6 GB | Q8_0 | Comfortable |
|
29.6
tok/s
18–47 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 75.6 GB | Q8_0 | Comfortable |
|
29.6
tok/s
18–47 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 75.6 GB | Q8_0 | Comfortable |
|
29.5
tok/s
18–47 · low confidence |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 59.3 GB | Q6_K | Comfortable |
|
29.5
tok/s
18–47 · low confidence |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 59.3 GB | Q6_K | Comfortable |
|
28.3
tok/s
17–45 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 75.6 GB | Q8_0 | Comfortable |
|
26.1
tok/s
16–42 · low confidence |
GRID A100B NVIDIA | 48 GB | 1,870 GB/s | May 2020 | 43.0 GB | Q4_K_M | Tight |
|
25.5
tok/s
15–41 · low confidence |
A100 PCIe 40 GB NVIDIA | 40 GB | 1,560 GB/s | Jun 2020 | 34.9 GB | Q3_K_M | Tight |
|
25.5
tok/s
15–41 · low confidence |
A100 SXM4 40 GB NVIDIA | 40 GB | 1,560 GB/s | May 2020 | 34.9 GB | Q3_K_M | Tight |
|
25.5
tok/s
15–41 · low confidence |
A800 PCIe 40 GB NVIDIA | 40 GB | 1,560 GB/s | Nov 2022 | 34.9 GB | Q3_K_M | Tight |
|
25.1
tok/s
15–40 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 75.6 GB | Q8_0 | Comfortable |
|
25.1
tok/s
15–40 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 75.6 GB | Q8_0 | Comfortable |
|
25.1
tok/s
15–40 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 75.6 GB | Q8_0 | Comfortable |
|
23.8
tok/s
14–38 · low confidence |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 75.6 GB | Q8_0 | Tight |
|
21.8
tok/s
13–35 · low confidence |
H100 SXM5 64 GB NVIDIA | 64 GB | 2,020 GB/s | Mar 2023 | 51.2 GB | Q5_K_M | Tight |
|
20.3
tok/s
12–33 · low confidence |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 75.6 GB | Q8_0 | Tight |
|
20.3
tok/s
12–33 · low confidence |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 75.6 GB | Q8_0 | Tight |
|
20.3
tok/s
12–33 · low confidence |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 75.6 GB | Q8_0 | Tight |
|
18.7
tok/s
11–30 · low confidence |
RTX PRO 5000 Blackwell NVIDIA | 48 GB | 1,340 GB/s | Mar 2025 | 43.0 GB | Q4_K_M | Tight |
|
17.9
tok/s
11–29 · low confidence |
A100 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Nov 2020 | 59.3 GB | Q6_K | Comfortable |
|
17.9
tok/s
11–29 · low confidence |
A100X NVIDIA | 80 GB | 2,040 GB/s | Jun 2021 | 59.3 GB | Q6_K | 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
- Tigerobo
- Organisation type
- Industry
- Country
- China
- Published
- 6 September 2023
- Authors
- Ye Chen, Wei Cai, Liangmin Wu, Xiaowei Li, Zhanxuan Xin, Cong Fu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Chat, Language generation, Language modeling/generation, Question answering
- Base model
- Llama 2-70B
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
- 70B
- Training data
- 300,000,000,000 tokens
- Batch size
- 4,000,000
70B
from paper: "We pretrained TigerBot models using a global batch size (GBS) of 4M tokens, while fine-tuned models with a GBS as small as 100–400k tokens" It's also based on pretrained Llama 2, which also used a batch size of 4M
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
- 1 × 10²⁴ FLOP
- How it was established
- Operation counting
- Fine-tuning compute
- 1.3 × 10²³ FLOP
~1.02e24 Tigerobo did ~2.1e23 additional pre-training. We estimated Llama 2 was trained on 8.1e23 FLOP.
70b * 300b * 6 = 126000*10^18 = 1.26*10^23
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
- Chips used
- 512
- Power draw
- 406.9 kW
- Data centre
- Paper on TigerBot-70B
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 source
Apache 2.0 https://github.com/TigerResearch/TigerBot/blob/main/README_en.md but it's also a Llama 2 finetune. training code here: https://github.com/TigerResearch/TigerBot/tree/main/train They released a 5% sample of training data: " On this basis, we randomly sampled 100G of data and released it open source."
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
- TigerBot: An Open Multilingual Multitask LLM
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run TigerBot-70B
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 48.4 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 48.4 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 38.7 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 38.7 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 30.9 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 29.6 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 29.6 tok/s
- 08 H800 SXM5 80 GB · 3,360 GB/s · Q6_K 29.5 tok/s
- 09 H100 SXM5 80 GB 80 GB · 3,360 GB/s · Q6_K 29.5 tok/s
- 10 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 28.3 tok/s
The smallest GPUs that still run TigerBot-70B
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 A800 PCIe 40 GB 40 GB · needs 34.9 GB · Q3_K_M · tight 25.5 tok/s
- 02 A100 PCIe 40 GB 40 GB · needs 34.9 GB · Q3_K_M · tight 25.5 tok/s
- 03 A100 SXM4 40 GB 40 GB · needs 34.9 GB · Q3_K_M · tight 25.5 tok/s
- 04 Radeon PRO W7900D 48 GB · needs 43.0 GB · Q4_K_M · tight 9.4 tok/s
- 05 RTX PRO 5000 Blackwell 48 GB · needs 43.0 GB · Q4_K_M · tight 18.7 tok/s
- 06 RTX 5880 Ada Generation 48 GB · needs 43.0 GB · Q4_K_M · tight 12.1 tok/s
- 07 L20 48 GB · needs 43.0 GB · Q4_K_M · tight 12.1 tok/s
- 08 Radeon PRO W7800 48 GB 48 GB · needs 43.0 GB · Q4_K_M · tight 9.4 tok/s
- 09 Radeon PRO W7900 48 GB · needs 43.0 GB · Q4_K_M · tight 9.4 tok/s
- 10 Data Center GPU Max 1100 48 GB · needs 43.0 GB · Q4_K_M · tight 11.2 tok/s
What the numbers mean
The hardware side
Minimum card
A100 PCIe 40 GB
Memory needed
34.9 GB
Fastest
48.4 tok/s
TigerBot-70B sits at 70B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 61 of the cards we track can hold it.
At the low end, a A100 PCIe 40 GB handles it — 40 GB, at Q3_K_M, for about 25.5 tokens per second.
At the other end, a B200 generates roughly 48.4 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.
Where it came from
TigerBot-70B was published by Tigerobo, in China, in September 2023. The organisation is categorised as industry.
It works in Language, and is recorded as doing chat, Language generation, Language modeling/generation, Question answering.
It builds on Llama 2-70B, which is why it shares that model's general shape and size.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
Understanding the speeds
Across every card that can run it, the middle of the range is about 17.1 tokens per second, and 50 of them clear the ten tokens per second that roughly matches reading speed.
Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.
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 around 1 × 10²⁴ FLOP of arithmetic, on NVIDIA A100, which is a statement about the training budget rather than about inference.
The training set ran to roughly 300,000,000,000 tokens.
Step by step
How to choose a GPU for TigerBot-70B
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
The table lists every card that can hold TigerBot-70B — around 34.9 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
-
02
Decide how long your conversations run
Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for TigerBot-70B.
-
03
Set a quality floor
Each card runs the least-compressed copy it can hold — Q3_K_M on the smallest card that fits. Setting a floor drops the cards that only manage TigerBot-70B by squeezing it further than you would want.
-
04
Rank by throughput rather than spec sheet
Ranking by tokens per second for TigerBot-70B follows memory bandwidth, not core counts, which is why the B200 tops it at 48.4 tok/s.
-
05
Read the fit column last
The fit column separates cards that just manage TigerBot-70B 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 TigerBot-70B alone — a card is usually bought for more than one model.
Answers
TigerBot-70B — common questions
Who created TigerBot-70B?
TigerBot-70B was published by Tigerobo, based in China, categorised as industry.
When was TigerBot-70B released?
TigerBot-70B was published in September 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 TigerBot-70B used for?
TigerBot-70B works in Language, and is recorded as handling chat, Language generation, Language modeling/generation, Question answering. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Where can I download TigerBot-70B?
The weights for TigerBot-70B 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 TigerBot-70B?
Around 1 × 10²⁴ FLOP, on NVIDIA A100. 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 TigerBot-70B if it does not fit in my GPU?
Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded TigerBot-70B is rarely worth using — the nearest miss we calculate is short by 14.2 GB. Every figure here assumes the whole model is on the card.
Would two GPUs run TigerBot-70B faster?
Capacity adds across cards; throughput does not. Since 61 of the cards we track already hold TigerBot-70B on their own, a second card is rarely the answer here.
Why does the quantisation differ between cards for TigerBot-70B?
Because capacity varies, so does how hard TigerBot-70B has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these TigerBot-70B speed estimates?
These are estimates with real error bars. The fastest result here, 29–77 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 TigerBot-70B?
The smallest card in our catalogue that holds TigerBot-70B is the A100 PCIe 40 GB, with 40 GB of memory. It runs the model at Q3_K_M using about 34.9 GB, and produces roughly 25.5 tokens per second. 61 cards in total can run it.
How fast is TigerBot-70B on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 48.4 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 50 of the cards that can run TigerBot-70B clear that.
How much VRAM does TigerBot-70B need?
About 34.9 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 TigerBot-70B open source?
Its weights are published, so TigerBot-70B 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 TigerBot-70B have?
TigerBot-70B has 70B parameters. 70B. 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.
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.