Calculate the TPS of the Radeon R9 M385 on local AI models

AMD 4 GB GDDR5 77 GB/s May 2015

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

105 models it can run

721 models in our catalogue altogether

Largest model it holds

DeciLM 6B

5.7B · Q3_K_M · 12.0 tok/s

Fastest model

Gemma 4 E2B

29.8 tok/s · 5.1B

Which AI models can run on a Radeon R9 M385?

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
29.8 tok/s

18–48 · low confidence

Gemma 4 E2B ≈ 5.1B Apr 2026 3.4 GB 11k tokens ? Q3_K_M Tight
25.4 tok/s

15–41 · low confidence

Gemma 3 1B 1B Mar 2025 1.8 GB 33k tokens Q8_0 Comfortable
25.4 tok/s

15–41 · low confidence

Gemma 3 QAT 1B 1B Apr 2025 1.8 GB 33k tokens Q8_0 Comfortable
25.4 tok/s

15–41 · low confidence

HGRN 1B (WT 103) ≈ 1B Nov 2023 1.8 GB 131k tokens ? Q8_0 Comfortable
25.4 tok/s

15–41 · low confidence

LLama 3..2 Typhoon 2 1B ≈ 1B Dec 2024 1.8 GB 131k tokens ? Q8_0 Comfortable
25.4 tok/s

15–41 · low confidence

OLMo-1B ≈ 1B Feb 2024 1.8 GB 131k tokens ? Q8_0 Comfortable
25.4 tok/s

15–41 · low confidence

Pythia-1b ≈ 1B Apr 2023 1.8 GB 131k tokens ? Q8_0 Comfortable
23.5 tok/s

14–38 · low confidence

OpenELM-1.1B ≈ 1.1B May 2024 1.9 GB 131k tokens ? Q8_0 Comfortable
23.1 tok/s

14–37 · low confidence

DeciCoder-1B ≈ 1.1B Aug 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
23.1 tok/s

14–37 · low confidence

SantaCoder ≈ 1.1B Jan 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
23.1 tok/s

14–37 · low confidence

TinyLlama-1.1B (1T token checkpoint) ≈ 1.1B Oct 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
23.1 tok/s

14–37 · low confidence

TinyLlama-1.1B (3T token checkpoint) ≈ 1.1B Oct 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
21.1 tok/s

13–34 · low confidence

EXAONE 4.0 (1.2B) ≈ 1.2B Jul 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
21.1 tok/s

13–34 · low confidence

LFM2-1.2B ≈ 1.2B Jul 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
21.1 tok/s

13–34 · low confidence

MinerU2.5 ≈ 1.2B Sep 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
21.1 tok/s

13–34 · low confidence

Pleias 1.0 1.2B ≈ 1.2B Dec 2024 2.0 GB 131k tokens ? Q8_0 Comfortable
21.1 tok/s

13–34 · low confidence

Pleias-RAG-1B ≈ 1.2B Apr 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
20.6 tok/s

12–33 · low confidence

Llama 3.2 1B 1.2B Sep 2024 2.2 GB 54k tokens Q8_0 Comfortable
20.3 tok/s

12–33 · low confidence

MiniCPM-1.2B ≈ 1.2B Jun 2024 2.0 GB 131k tokens ? Q8_0 Comfortable
19.5 tok/s

12–31 · low confidence

DeepSeek Coder 1.3B ≈ 1.3B Jan 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
19.5 tok/s

12–31 · low confidence

DeepSeek-VL-1.3B ≈ 1.3B Mar 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
19.5 tok/s

12–31 · low confidence

DigiRL ≈ 1.3B Jun 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
19.5 tok/s

12–31 · low confidence

GLA Transformer 1.3B ≈ 1.3B Aug 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
19.5 tok/s

12–31 · low confidence

Janus 1.3B ≈ 1.3B Oct 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
19.5 tok/s

12–31 · 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

Radeon R9 M385 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
77 GB/s
Memory type
GDDR5
Memory bus width
128 bit
Memory clock
1.2 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
Strato
Architecture
GCN 2.0
Generation
Gem System(R9 M300)
Foundry
TSMC
Process size
28 nm
Transistors
2.1 billion
Transistor density
13,000 K/mm²
Die size
160 mm²
Package
FCBGA-1093
Released
5 May 2015

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
900 MHz
Boost clock
1 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
896
Texture mapping units
56
Render output units
16
L1 cache
16 KB
L2 cache
0.25 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)
1.8 TFLOPS
Double precision (FP64)
112 GFLOPS
Pixel rate
16 GPixel/s
Texture rate
56 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.

Bus interface
PCIe 3.0 x16

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.

DirectX
12.0
OpenGL
4.6
Vulkan
1.2
OpenCL
2.1
Shader model
6.5

Listings

Where to buy a Radeon R9 M385

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

What the memory subsystem means for AI

Memory

4 GB

Bandwidth

77 GB/s

Largest model

DeciLM 6B

Radeon R9 M385 carries only 4 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 3.6 GB.

Memory bandwidth reaches 77 GB/s across a bus of 128 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.

That comes from a memory clock of 1.2 GHz. It is why core counts predict generation speed so poorly.

In practice that combination tops out at DeciLM 6B, 5.7B, compressed to Q3_K_M and generating around 12.0 tokens per second.

The chip and how it was built

Radeon R9 M385 is built on the graphics processor Strato, using the architecture GCN 2.0 from AMD, as part of the generation Gem System(R9 M300).

The chip is manufactured by TSMC, on a process of 28 nm, with a die measuring 160 mm², holding 2.1 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 May 2015, roughly 11.360911305534 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

112 GFLOPS

Double-precision throughput reaches 112 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 900 MHz to a boost of 1 GHz. 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

Radeon R9 M385 has an L1 cache of 16 KB, backed by an L2 cache of 0.25 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 896 shading units, 56 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

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 Radeon R9 M385

The biggest open-weight models that fit on this card, newest first. Each is shown at the best compression the card can hold.

  1. 01 Gemma 4 E2B 5.1B · Q3_K_M · Apr 2026 29.8 tok/s
  2. 02 Qwen3.5-4B 4B · Q5_K_M · Feb 2026 11.3 tok/s
  3. 03 Nemotron 3 Nano-4B 4B · Q5_K_M · Dec 2025 11.3 tok/s
  4. 04 Qwen3-VL-4B 4B · Q5_K_M · Oct 2025 11.3 tok/s
  5. 05 Qwen3-4B-Thinking-2507 4B · Q5_K_M · Aug 2025 11.3 tok/s
  6. 06 Voxtral Mini 4.7B · Q4_K_M · Jul 2025 12.5 tok/s
  7. 07 Phi-4-Multimodal 5.6B · Q3_K_M · Mar 2025 12.2 tok/s
  8. 08 Minitron 4B 4.2B · Q4_K_M · Nov 2024 14.0 tok/s
  9. 09 XVERSE-MoE-A4.2B 4.2B · Q4_K_M · Apr 2024 14.0 tok/s
  10. 10 DeciLM 6B 5.7B · Q3_K_M · Sep 2023 12.0 tok/s

The fastest AI models on a Radeon R9 M385

Where this card produces tokens quickest. Smaller models dominate here, because generating each token means reading the whole model out of memory once.

  1. 01 Gemma 4 E2B 5.1B · Q3_K_M · 3.4 GB 29.8 tok/s
  2. 02 Gemma 3 QAT 1B 1B · Q8_0 · 1.8 GB 25.4 tok/s
  3. 03 Gemma 3 1B 1B · Q8_0 · 1.8 GB 25.4 tok/s
  4. 04 LLama 3..2 Typhoon 2 1B 1B · Q8_0 · 1.8 GB 25.4 tok/s
  5. 05 OLMo-1B 1B · Q8_0 · 1.8 GB 25.4 tok/s
  6. 06 HGRN 1B (WT 103) 1B · Q8_0 · 1.8 GB 25.4 tok/s
  7. 07 Pythia-1b 1B · Q8_0 · 1.8 GB 25.4 tok/s
  8. 08 OpenELM-1.1B 1.1B · Q8_0 · 1.9 GB 23.5 tok/s
  9. 09 TinyLlama-1.1B (1T token checkpoint) 1.1B · Q8_0 · 1.9 GB 23.1 tok/s
  10. 10 TinyLlama-1.1B (3T token checkpoint) 1.1B · Q8_0 · 1.9 GB 23.1 tok/s

Step by step

How to work out the tokens per second of a Radeon R9 M385

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.

  1. 01

    Search for the model you want

    The table lists 105 models this card runs. Search narrows the list by name or by size.

  2. 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 against a card holding 4 GB it is often what pushes a large model over the edge.

  3. 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.

  4. 04

    Look at the range, not just the number

    The figures are calculated, not measured. The fastest result on this card is 29.8 tok/s on Gemma 4 E2B. The same card and model vary by thirty to fifty per cent between inference runtimes.

  5. 05

    Read the fit verdict last

    Tight means it works today; comfortable means it still works when the conversation grows. Compare what each model needs against an available 4 GB.

  6. 06

    Open the model to compare cards

    Each model page repeats this calculation for the whole catalogue. Worth a look before deciding: it shows what else runs the same model, alongside Radeon R9 M385.

Answers

Radeon R9 M385 — common questions

01

Radeon R9 M385— how much memory does it have?

This card has 4 GB of GDDR5. Around a tenth is reserved by the inference runtime and the driver, leaving roughly 3.6 GB available for a model and its conversation.

02

Radeon R9 M385— what is its memory bandwidth?

Memory bandwidth reaches 77 GB/s across a bus of 128 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.

03

Radeon R9 M385— what type of memory does it use?

It uses GDDR5 clocked at 1.2 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.

04

Radeon R9 M385— who makes it?

This is a product of AMD, with the chip manufactured by TSMC, on a process of 28 nm.

05

Radeon R9 M385— when was it released?

It was released in May 2015.

06

Radeon R9 M385— how much cache does it have?

The L1 cache is 16 KB, and the L2 cache is 0.25 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.

07

Radeon R9 M385— does it support CUDA?

No. CUDA is NVIDIA-only, and this is a card from AMD. It runs language models through ROCm, Vulkan or Metal depending on the software, which are less mature than the CUDA path — our estimates apply a penalty for that.

08

Radeon R9 M385— what bus interface does it 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.

09

Radeon R9 M385— 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.

10

Radeon R9 M385— can it run a model that does not fit in its memory?

Only partly. Layers beyond 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.

11

Would two Radeon R9 M385 cards be twice as fast?

No. A second card doubles the memory to 8 GB which lets you hold models neither could hold alone, but generation does not split that way. These figures describe one card.

12

Radeon R9 M385— 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.

13

Radeon R9 M385— 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 12.0 tokens per second and needs about 3.5 GB of the card's memory.

14

Radeon R9 M385— 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 29.8 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.

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.

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