Calculate the TPS of the Switch GPU 20nm 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
DeciLM 6B
5.7B · Q3_K_M · 4.4 tok/s
Fastest model
Gemma 4 E2B
10.8 tok/s · 5.1B
Which AI models can run on a Switch GPU 20nm?
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.
105 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
10.8
tok/s
4–22 · low confidence |
Gemma 4 E2B ≈ | 5.1B | Apr 2026 | 3.4 GB | 11k tokens ? | Q3_K_M | Tight |
|
9.2
tok/s
3–18 · low confidence |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
9.2
tok/s
3–18 · low confidence |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
9.2
tok/s
3–18 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
9.2
tok/s
3–18 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
9.2
tok/s
3–18 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
9.2
tok/s
3–18 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
8.5
tok/s
3–17 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
8.4
tok/s
3–17 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
8.4
tok/s
3–17 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
8.4
tok/s
3–17 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
8.4
tok/s
3–17 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.7
tok/s
3–15 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.7
tok/s
3–15 · low confidence |
LFM2-1.2B ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.7
tok/s
3–15 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.7
tok/s
3–15 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.7
tok/s
3–15 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.5
tok/s
3–15 · low confidence |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 54k tokens | Q8_0 | Comfortable |
|
7.4
tok/s
3–15 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.1
tok/s
2–14 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.1
tok/s
2–14 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.1
tok/s
2–14 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.1
tok/s
2–14 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.1
tok/s
2–14 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
7.1
tok/s
2–14 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 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
Switch GPU 20nm 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
- 26 GB/s
- Memory type
- DDR4
- Memory bus width
- 64 bit
- Memory clock
- 1.6 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
- GM20B
- Architecture
- Maxwell 2.0
- Generation
- Console GPU(Nintendo)
- Foundry
- TSMC
- Process size
- 20 nm
- Transistors
- 2 billion
- Transistor density
- 16,900 K/mm²
- Die size
- 118 mm²
- Released
- 17 March 2017
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
- 384 MHz
- Boost clock
- 768 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
- 256
- Texture mapping units
- 16
- Render output units
- 16
- Streaming multiprocessors
- 2
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)
- 786.4 GFLOPS
- Single precision (FP32)
- 393.2 GFLOPS
- Double precision (FP64)
- 12.3 GFLOPS
- Pixel rate
- 12 GPixel/s
- Texture rate
- 12 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)
- 15 W
- Dimensions
- 239 mm × 28 mm
- Display outputs
- 1x USB Type-C
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
- 5.3
- DirectX
- 12.1
- OpenGL
- 4.6
- Vulkan
- 1.4
- OpenCL
- 1.2
- Shader model
- 6.0
Listings
Where to buy a Switch GPU 20nm
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
26 GB/s
Largest model
DeciLM 6B
Switch GPU 20nm carries only 4 GB of DDR4. 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 3.6 GB.
Memory bandwidth reaches 26 GB/s across a bus of 64 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 1.6 GHz. Widening the bus and raising the clock are the two levers a manufacturer has, which is why a card with unremarkable cores can still generate quickly.
The biggest thing it holds is DeciLM 6B, 5.7B, compressed to Q3_K_M and generating around 4.4 tokens per second.
The chip and how it was built
Switch GPU 20nm is built on the graphics processor GM20B, using the architecture Maxwell 2.0 from NVIDIA, as part of the generation Console GPU(Nintendo).
The chip is manufactured by TSMC, on a process of 20 nm, with a die measuring 118 mm², holding 2 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 March 2017, roughly 9.4950395403261 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
786.4 GFLOPS
FP64
12.3 GFLOPS
On paper Switch GPU 20nm reaches 786.4 GFLOPS at half precision, and 393.2 GFLOPS 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 reaches 12.3 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 384 MHz to a boost of 768 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
There are 256 shading units, 16 texture mapping units, and 16 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
15 W
Switch GPU 20nm is rated at 15 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.
measuring 239 mm long. 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.
The extremes
The largest AI models that run on a Switch GPU 20nm
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 Switch GPU 20nm
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 Switch GPU 20nm
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 105 models the card handles. The search box takes a name or a size such as 27b, which matches on parameter count.
-
02
Set the context length you will actually use
Drag the slider to the conversation length you plan to work at. The cache grows with the conversation, and against a card holding 4 GB it is often what pushes a large model over the edge.
-
03
Set a minimum quality if you need one
By default the table picks the least-compressed copy that fits. Setting a floor removes models that only qualify through heavy compression.
-
04
Look at the range, not just the number
The figures are calculated, not measured. The fastest result on this card is 10.8 tok/s on Gemma 4 E2B. The same card and model vary by thirty to fifty per cent between inference runtimes.
-
05
Check the headroom before you decide
The fit column separates models that just fit from those with room to spare — worth checking before settling on one, against an available 4 GB.
-
06
Check the same model from the other side
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 Switch GPU 20nm.
Answers
Switch GPU 20nm — common questions
Switch GPU 20nm— what are its TFLOPS?
It is rated at 786.4 GFLOPS at half precision and 393.2 GFLOPS 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.
Switch GPU 20nm— does it support CUDA?
Yes. It reports CUDA compute capability 5.3, 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.
Switch GPU 20nm— 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 105 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Switch GPU 20nm— can it run a model that does not fit in its memory?
Offloading past the card's 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 Switch GPU 20nm cards be twice as fast?
Capacity adds, throughput does not. Two of them give you 8 GB of combined memory, at roughly the same generation speed as one.
Switch GPU 20nm— which AI models can it run?
105 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.
Switch GPU 20nm— what is the largest AI model it can run?
The largest model in our catalogue that fits is DeciLM 6B at 5.7B parameters, compressed to Q3_K_M. It generates roughly 4.4 tokens per second and needs about 3.5 GB of the card's memory.
Switch GPU 20nm— how many tokens per second does it produce?
It depends on the model. The fastest model we track here is Gemma 4 E2B at about 10.8 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.
Switch GPU 20nm— how much memory does it have?
This card has 4 GB of DDR4. Around a tenth is reserved by the inference runtime and the driver, leaving roughly 3.6 GB available for a model and its conversation.
Switch GPU 20nm— what is its memory bandwidth?
Memory bandwidth reaches 26 GB/s across a bus of 64 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.
Switch GPU 20nm— what type of memory does it use?
It uses DDR4 clocked at 1.6 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.
Switch GPU 20nm— who makes it?
This is a product of NVIDIA, with the chip manufactured by TSMC, on a process of 20 nm.
Switch GPU 20nm— when was it released?
It was released in March 2017.
Switch GPU 20nm— how much power does it use?
Rated board power is 15 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.
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.