Calculate the TPS of the Tesla M2070-Q on local AI models
Every model in our catalogue assessed against this card at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from this card's memory bandwidth and the size of each model once compressed.
Calculated for this card
721 models in our catalogue altogether
Largest model it holds
Qwen-VL
9.6B · Q3_K_M · 15.2 tok/s
Fastest model
Gemma 3 QAT 1B
54.1 tok/s · 1B
Which AI models can run on a Tesla M2070-Q?
Set the inputs, read the answer
More context means more memory for the conversation cache. Speed is for a fresh conversation and does not change with this setting.
Hides models that would only fit by being compressed below this point.
280 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
54.1
tok/s
19–108 · low confidence |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
54.1
tok/s
19–108 · low confidence |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
54.1
tok/s
19–108 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
54.1
tok/s
19–108 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
54.1
tok/s
19–108 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
54.1
tok/s
19–108 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
50.1
tok/s
18–100 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
49.2
tok/s
17–98 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
49.2
tok/s
17–98 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
49.2
tok/s
17–98 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
49.2
tok/s
17–98 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
45.1
tok/s
16–90 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
45.1
tok/s
16–90 · low confidence |
LFM2-1.2B ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
45.1
tok/s
16–90 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
45.1
tok/s
16–90 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
45.1
tok/s
16–90 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
44.0
tok/s
15–88 · low confidence |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 113k tokens | Q8_0 | Comfortable |
|
43.4
tok/s
15–87 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
41.6
tok/s
15–83 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
41.6
tok/s
15–83 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
41.6
tok/s
15–83 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
41.6
tok/s
15–83 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
41.6
tok/s
15–83 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
41.6
tok/s
15–83 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
41.6
tok/s
15–83 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | 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
Tesla M2070-Q full specification
Everything on record for this board, ordered by how much it bears on running a language model rather than by how a spec sheet would list it. Memory comes first because it decides the outcome; the rest is context.
Memory
The two specifications that decide what this card can run and how quickly. Capacity sets which models fit; bandwidth sets how many tokens per second they produce once they do.
- Memory size
- 6 GB
- Memory bandwidth
- 150 GB/s
- Memory type
- GDDR5
- Memory bus width
- 384 bit
- Memory clock
- 783 MHz
The chip
Which processor is on the board and how it was manufactured. A smaller process size generally means more performance for the same power.
- Graphics processor
- GF100
- Architecture
- Fermi
- Generation
- Tesla Fermi(x20xx)
- Foundry
- TSMC
- Process size
- 40 nm
- Transistors
- 3.1 billion
- Transistor density
- 5,900 K/mm²
- Die size
- 529 mm²
- Package
- BGA-1980
- Released
- 25 July 2011
Clock speeds
How fast the processor runs. Worth far less here than on a gaming benchmark: generating text is limited by memory bandwidth, so a higher clock barely moves the result.
- Base clock
- 574 MHz
- Boost clock
- 574 MHz
Processing units
What the chip contains. These drive graphics performance and matter mainly for processing a long prompt rather than for producing the answer.
- Shading units
- 448
- Texture mapping units
- 56
- Render output units
- 48
- Streaming multiprocessors
- 14
- L1 cache
- 64 KB
- L2 cache
- 0.75 MB
Theoretical performance
Peak arithmetic rates published for the board. These are ceilings that no real workload reaches, and generating text reaches a small fraction of them because it is limited by memory rather than arithmetic.
- Single precision (FP32)
- 1 TFLOPS
- Double precision (FP64)
- 513.9 GFLOPS
- Pixel rate
- 16 GPixel/s
- Texture rate
- 32 GTexel/s
The board
What it takes to physically install and power the card — the practical constraints that decide whether it fits the machine you already own.
- Power draw (TDP)
- 225 W
- Suggested power supply
- 550 W
- Power connectors
- 1x 6-pin + 1x 8-pin
- Bus interface
- PCIe 2.0 x16
- Slot width
- Dual-slot
- Dimensions
- 248 mm
Software support
Which graphics and compute interfaces the card supports. CUDA compute capability is the one that bears on inference: below 7.0 there are no tensor cores, and modern inference software falls back to slower code paths.
- CUDA compute capability
- 2.0
- DirectX
- 11.0
- OpenGL
- 4.6
- OpenCL
- 1.1
- Shader model
- 5.1
Listings
Where to buy a Tesla M2070-Q
No vendor is currently listing this card. Listings come from vendors who publish them here directly — browse the vendor directory to see who is selling what.
What the numbers mean
Capacity and bandwidth
Memory
6 GB
Bandwidth
150 GB/s
Largest model
Qwen-VL
Tesla M2070-Q carries only 6 GB of GDDR5. That limits it to the smaller end of the catalogue, and a model has to fit entirely inside before it generates anything at all. A runtime actually gets about 5.4 GB.
Memory bandwidth reaches 150 GB/s across a bus of 384 bits. Bandwidth is this card's real constraint. Every token requires reading the entire model out of memory, so a large model will feel slow here even when it fits.
The figure is the bus width multiplied by a memory clock of 783 MHz. Both halves matter, and neither is visible in a gaming benchmark.
The biggest thing it holds is Qwen-VL, 9.6B, compressed to Q3_K_M and generating around 15.2 tokens per second.
The chip and how it was built
Tesla M2070-Q is built on the graphics processor GF100, using the architecture Fermi from NVIDIA, as part of the generation Tesla Fermi(x20xx).
The chip is manufactured by TSMC, on a process of 40 nm, with a die measuring 529 mm², holding 3.1 billion transistors. A smaller process generally means more performance for the same power, though for language models it matters far less than the memory subsystem.
It was released in July 2011, roughly 15.138994279347 years ago. Inference software support tends to follow hardware by a year or two, so a card of this age generally has mature, well-optimised code paths available to it.
Compute throughput, and why it matters less than it looks
FP64
513.9 GFLOPS
Double-precision throughput reaches 513.9 GFLOPS. It has no bearing on running a language model — no inference runtime uses it — but it separates datacentre parts from consumer ones, since the latter deliberately restrict it.
Clocks run from a base of 574 MHz to a boost of 574 MHz. Worth far less here than on a gaming benchmark: raising the clock speeds up the arithmetic, and the arithmetic is not what generation is waiting on.
Cache and processing units
Tesla M2070-Q has an L1 cache of 64 KB, backed by an L2 cache of 0.75 MB. Cache absorbs a share of the memory traffic that would otherwise hit the main bus, which is the one place on this page where a number other than bandwidth quietly affects generation speed — a large L2 lets more of the working set stay close to the cores.
There are 448 shading units, 56 texture mapping units, and 48 render output units. These drive graphics workloads and contribute to prompt processing, but they sit idle for much of the time a model spends generating a reply.
Power, size and installation
Power draw
225 W
Tesla M2070-Q is rated at 225 W, and the suggested system power supply is 550 W. Running a language model keeps a card busy in bursts rather than continuously — it draws hard while generating and idles between requests — so sustained draw over a working day is usually well below the rated figure.
The board occupies dual-slot, measuring 248 mm long, and needs 1x 6-pin + 1x 8-pin. Worth checking against the case and power supply already in the machine, since the largest cards need considerably more of both than a typical desktop provides.
It connects over PCIe 2.0 x16. The interface governs how quickly a model is loaded from disk into the card, not how fast it runs once there, so a narrower link costs a few seconds at startup and nothing thereafter.
The extremes
The largest AI models that run on a Tesla M2070-Q
The biggest open-weight models that fit on this card, newest first. Each is shown at the best compression the card can hold.
The fastest AI models on a Tesla M2070-Q
Where this card produces tokens quickest. Smaller models dominate here, because generating each token means reading the whole model out of memory once.
Step by step
How to work out the tokens per second of a Tesla M2070-Q
You do not have to calculate anything by hand — the gputps.com calculator on this page has already worked it out for every model this card can hold. Reading off the answer takes six steps.
-
01
Start with the model, not the specification
The table lists 280 models this card runs. Search narrows the list by name or by size.
-
02
Decide how long your conversations run
Set the context to your real working length. Short questions cost almost nothing, but a long document can consume a large share of 6 GB that is frequently the difference between a model fitting and not.
-
03
Choose how far you will compress
Each model is shown at the best compression this card can hold. A minimum quality hides the ones that only fit by being squeezed further than you would accept.
-
04
Read the speed and the range
Each speed is an estimate for a single conversation, with a range beneath it. The top end here is 54.1 tok/s on Gemma 3 QAT 1B. The same card and model vary by thirty to fifty per cent between inference runtimes.
-
05
Check the memory column before committing
A tight fit runs but leaves no room to raise the context later; comfortable has headroom. The memory column shows what each model needs against an available 6 GB.
-
06
Open the model to compare cards
Every model name in the table links to its own page, which runs the same calculation across every card we hold. That is where you see whether the right buy is Tesla M2070-Q.
Answers
Tesla M2070-Q — common questions
Tesla M2070-Q— which AI models can it run?
280 of the 721 open-weight language models we track fit on this card and can be run locally. The table on this page lists every one, with the memory it needs, the quantisation it runs at and an estimated generation speed.
Tesla M2070-Q— what is the largest AI model it can run?
The largest model in our catalogue that fits is Qwen-VL at 9.6B parameters, compressed to Q3_K_M. It generates roughly 15.2 tokens per second and needs about 5.4 GB of the card's memory.
Tesla M2070-Q— how many tokens per second does it produce?
It depends on the model. The fastest model we track here is Gemma 3 QAT 1B at about 54.1 tokens per second, while larger models run proportionally slower because each token requires reading the whole model out of memory once. Speeds are estimates for a single conversation at a time.
Tesla M2070-Q— can it run 7B models?
Yes. For example it runs Gemma 4 E4B at Q3_K_M, using about 5.2 GB of memory and generating around 32.5 tokens per second.
Tesla M2070-Q— how much memory does it have?
This card has 6 GB of GDDR5. Around a tenth is reserved by the inference runtime and the driver, leaving roughly 5.4 GB available for a model and its conversation.
Tesla M2070-Q— what is its memory bandwidth?
Memory bandwidth reaches 150 GB/s across a bus of 384 bits. This is the single best predictor of how fast it generates text, because producing each token means reading the entire model out of memory once.
Tesla M2070-Q— what type of memory does it use?
It uses GDDR5 clocked at 783 MHz. HBM types are found on datacentre accelerators and carry far more bandwidth than the GDDR used on desktop cards, which is why they generate tokens considerably faster at the same capacity.
Tesla M2070-Q— who makes it?
This is a product of NVIDIA, with the chip manufactured by TSMC, on a process of 40 nm.
Tesla M2070-Q— when was it released?
It was released in July 2011.
Tesla M2070-Q— how much power does it use?
Rated board power is 225 W, and the suggested system power supply is 550 W. Generating text draws hard in bursts and idles between requests, so average consumption over a working session is normally well below the rated figure.
Tesla M2070-Q— how much cache does it have?
The L1 cache is 64 KB, and the L2 cache is 0.75 MB. Cache absorbs part of the memory traffic that would otherwise reach the main bus, so a larger L2 gives a modest lift to generation speed beyond what bandwidth alone predicts.
Tesla M2070-Q— does it support CUDA?
Yes. It reports CUDA compute capability 2.0, which predates tensor cores. Capability 7.0 and above has tensor cores, which modern inference software uses; below that it falls back to slower code paths for quantised models.
Tesla M2070-Q— what bus interface does it use?
It uses PCIe 2.0 x16. This governs how fast a model is loaded onto the card rather than how fast it runs once loaded, so it costs a few seconds at startup and nothing during generation.
Tesla M2070-Q— is it good for running local AI models?
Its memory limits it to smaller models though its bandwidth means generation will feel slow on larger models. In total it runs 280 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Tesla M2070-Q— can it run a model that does not fit in its memory?
It can be split, with the overflow held in system memory beyond the card's 6 GB sit in system memory and run at a fraction of the speed, so a mostly-offloaded model is rarely worth using. Every figure here assumes it is fully resident on the card.
Would two Tesla M2070-Q cards be twice as fast?
Pairing them buys headroom rather than pace: 12 GB to work with rather than twice the tokens per second — every figure here is for a single card.
The other direction
Looking at it from the other side?
This page starts from the hardware. If you already know which model you want and need to know what it takes to run it, start from the model instead.