Calculate the TPS of the GRID RTX T10-4 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
679 models in our catalogue altogether
Largest model it holds
DeciLM 6B
5.7B · Q3_K_M · 135 tok/s
Fastest model
Gemma 3 QAT 1B
285 tok/s · 1B
Which AI models can run on a GRID RTX T10-4?
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.
97 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
285
tok/s
242–342 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
285
tok/s
242–342 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
285
tok/s
171–455 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
285
tok/s
171–455 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
285
tok/s
171–455 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
285
tok/s
171–455 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
264
tok/s
158–422 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
259
tok/s
155–414 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
259
tok/s
155–414 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
259
tok/s
155–414 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
259
tok/s
155–414 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
237
tok/s
142–379 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
237
tok/s
142–379 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
237
tok/s
142–379 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
237
tok/s
142–379 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
231
tok/s
197–278 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 54k tokens | Q8_0 | Comfortable |
|
228
tok/s
137–365 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
219
tok/s
131–350 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
219
tok/s
131–350 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
219
tok/s
131–350 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
219
tok/s
131–350 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
219
tok/s
131–350 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
219
tok/s
131–350 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
219
tok/s
131–350 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
219
tok/s
131–350 · low confidence |
Phi-1 ≈ | 1.3B | Oct 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
GRID RTX T10-4 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
- 4 GB
- Memory bandwidth
- 672 GB/s
- Memory type
- GDDR6
- Memory bus width
- 384 bit
- Memory clock
- 1.75 GHz
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
- TU102
- Architecture
- Turing
- Generation
- GRID(Tx)
- Foundry
- TSMC
- Process size
- 12 nm
- Transistors
- 18.6 billion
- Transistor density
- 24,700 K/mm²
- Die size
- 754 mm²
- Released
- 1 January 2020
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
- 1.07 GHz
- Boost clock
- 1.4 GHz
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
- 3,584
- Texture mapping units
- 224
- Render output units
- 64
- Streaming multiprocessors
- 56
- Tensor cores
- 448
- Ray tracing cores
- 56
- L1 cache
- 64 KB
- L2 cache
- 6 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.
- Half precision (FP16)
- 20 TFLOPS
- Single precision (FP32)
- 10 TFLOPS
- Double precision (FP64)
- 312.5 GFLOPS
- Pixel rate
- 89 GPixel/s
- Texture rate
- 313 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)
- 150 W
- Suggested power supply
- 450 W
- Power connectors
- 1x 6-pin + 1x 8-pin
- Bus interface
- PCIe 3.0 x16
- Slot width
- Dual-slot
- Dimensions
- 267 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
- 7.5
- DirectX
- 12.2
- OpenGL
- 4.6
- Vulkan
- 1.4
- OpenCL
- 3.0
- Shader model
- 6.8
Listings
Where to buy a GRID RTX T10-4
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
Why memory is the number that matters here
Memory
4 GB
Bandwidth
672 GB/s
Largest model
DeciLM 6B
At 4 GB of GDDR6 the GRID RTX T10-4 is limited to the smaller end of the catalogue. About 3.6 GB is actually available to a runtime, and a model has to fit entirely inside it before generating anything at all.
The memory bus moves 672 GB/s across a 384-bit bus. That is the number that governs generation speed — arithmetic per byte read is small enough that the bus, not the cores, is what everything waits on.
The figure is the memory clock — 1.75 GHz here — multiplied by the bus width. It is why core counts predict generation speed so poorly.
The practical ceiling is DeciLM 6B at 5.7B, held at Q3_K_M and running at roughly 135 tokens per second.
The chip and how it was built
The GRID RTX T10-4 is built on the TU102 graphics processor, using NVIDIA's Turing architecture, as part of the GRID(Tx) generation.
The chip is manufactured by TSMC, on a 12 nm process, with a die measuring 754 mm², holding 18.6 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 January 2020, roughly 6 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
FP16
20 TFLOPS
FP64
312.5 GFLOPS
Tensor cores
448
On paper the GRID RTX T10-4 reaches 20 TFLOPS at half precision and 10 TFLOPS at single precision. These are peak figures no real workload sustains, and generating text reaches only a small fraction of them — decoding is limited by memory rather than arithmetic, which is why a card can look enormously powerful here and still produce tokens at an ordinary rate.
Double-precision throughput is 312.5 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.
The card carries 448 tensor cores across 56 streaming multiprocessors. These accelerate the matrix arithmetic at the heart of a transformer, and they are what make prompt processing — reading a long document before answering — dramatically faster than it would otherwise be.
Clocks run from 1.07 GHz at base to 1.4 GHz boosted. 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
The GRID RTX T10-4 has 64 KB of L1 cache, backed by 6 MB of L2. 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 3,584 shading units, 224 texture mapping units, and 64 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
150 W
The GRID RTX T10-4 is rated at 150 W, with a 450 W power supply suggested for the whole system. 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 a dual-slot, measuring 267 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 3.0 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 GRID RTX T10-4
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 GRID RTX T10-4
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 GRID RTX T10-4
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
Find the model in the table
All 97 models the GRID RTX T10-4 handles are already listed. The search box takes a name or a size such as 27b, which matches on parameter count.
-
02
Decide how long your conversations run
Drag the slider to the conversation length you plan to work at. The cache grows with the conversation, and on 4 GB it is often what pushes a large model over the edge.
-
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
Speeds come with error bars for a reason. The best case here is 285 tok/s on Gemma 3 QAT 1B, and which inference software you use moves that by thirty to fifty per cent.
-
05
Check the memory column before committing
The fit column separates models that just fit from those with room to spare — worth checking against the card's 4 GB before settling on one.
-
06
Open the model to compare cards
Following a model through to its own page lists all the hardware that can run it, so you can see where the GRID RTX T10-4 sits against the alternatives.
Answers
GRID RTX T10-4 — common questions
What is the memory bandwidth of a GRID RTX T10-4?
The GRID RTX T10-4 has 672 GB/s of memory bandwidth, across a 384-bit memory bus. 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.
What type of memory does a GRID RTX T10-4 use?
It uses GDDR6 clocked at 1.75 GHz. 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.
Who makes the GRID RTX T10-4?
The GRID RTX T10-4 is a NVIDIA product, with the chip manufactured by TSMC, on a 12 nm process.
When was the GRID RTX T10-4 released?
The GRID RTX T10-4 was released in January 2020.
How much power does a GRID RTX T10-4 use?
The GRID RTX T10-4 has a rated board power of 150 W, and a 450 W system power supply is suggested. Generating text draws hard in bursts and idles between requests, so average consumption over a working session is normally well below the rated figure.
How much cache does a GRID RTX T10-4 have?
The GRID RTX T10-4 has 64 KB of L1 cache, and 6 MB of L2 cache. 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.
What are the TFLOPS of a GRID RTX T10-4?
The GRID RTX T10-4 is rated at 20 TFLOPS at half precision and 10 TFLOPS at single precision. These are peak arithmetic ceilings rather than achievable rates, and text generation reaches only a small fraction of them because it is limited by memory bandwidth instead.
How many tensor cores does a GRID RTX T10-4 have?
The GRID RTX T10-4 has 448 tensor cores across 56 streaming multiprocessors. They accelerate the matrix arithmetic a transformer is built from, which mainly speeds up processing a long prompt rather than producing the reply.
Does the GRID RTX T10-4 support CUDA?
Yes. The GRID RTX T10-4 reports CUDA compute capability 7.5. 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.
What bus interface does the GRID RTX T10-4 use?
It uses PCIe 3.0 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.
Is the GRID RTX T10-4 good for running local AI models?
Its memory limits it to smaller models and its bandwidth gives usable, if unspectacular, generation speeds. In total it runs 97 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Can a GRID RTX T10-4 run a model that does not fit in its memory?
Only partly. Layers beyond the 4 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 GRID RTX T10-4 cards be twice as fast?
Capacity adds, throughput does not. Two of them give you 8 GB to work with rather than twice the tokens per second — every figure here is for a single GRID RTX T10-4.
What AI models can a GRID RTX T10-4 run?
97 of the 679 open-weight language models we track fit on a GRID RTX T10-4 and can be run locally on it. The table on this page lists every one, with the memory it needs, the quantisation it runs at and an estimated generation speed.
What is the largest AI model a GRID RTX T10-4 can run?
The largest model in our catalogue that fits on a GRID RTX T10-4 is DeciLM 6B at 5.7B parameters, compressed to Q3_K_M. It generates roughly 135 tokens per second and needs about 3.5 GB of the card's memory.
How many tokens per second does a GRID RTX T10-4 produce?
It depends on the model. On a GRID RTX T10-4 the fastest model we track is Gemma 3 QAT 1B at about 285 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.
How much memory does a GRID RTX T10-4 have?
A GRID RTX T10-4 has 4 GB of GDDR6 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 3.6 GB available for a model and its conversation.
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.