gpt-oss 20B on Intel Arc A770 16GB vs RTX 3060 12GB: Quant Table (2026)
gpt-oss 20B fits on a 12 GB RTX 3060 at short context. The A770's 16 GB only pays off once context grows.
By Mike Perry, Founder & Editor-in-Chief · Published 2026-11-10 · Updated 2026-11-10 · 9 min read
Quick Answer
For gpt-oss 20B at normal chat lengths, the RTX 3060 12GB is the better card. The native MXFP4 GGUF is 11.27 GiB and runs fully on a 3060 at 75 tok/s with a 2,048-token context, per an owner report in llama.cpp discussion #15396. The Arc A770 16GB's extra 4 GB only pays off at long contexts, where the 3060 has to offload.
Two searches drive interest in this matchup: "intel arc a770 llm" and "intel arc llm ollama". The A770 16GB is one of the cheapest ways to get 16 GB of VRAM on a desktop card, and gpt-oss 20B is the open-weight model most buyers want to run on a 12-16 GB card. OpenAI's gpt-oss-20b model card describes it as a 21B-parameter mixture-of-experts model with 3.6B active parameters per token that runs within 16 GB of memory. That raises the question this page answers: is 16 GB on Intel's slower software stack better than 12 GB on CUDA?
The answer depends on context length. The details follow.
Key Takeaways - gpt-oss 20B fits on a 12 GB card at short context. The ggml-org MXFP4 GGUF is 11.27 GiB, and an RTX 3060 owner reports 75 tok/s fully on the GPU at 2K context (llama.cpp #15396). - At 16K context, the 3060 starts to offload. The same owner reports 64 tok/s with two expert layers moved to the CPU. - The 3060 is faster on models that fit both cards. Its 7-9B median is 59.5 tok/s against the A770's 38.7 tok/s (SpecPicks benchmarks). - The A770 has more raw bandwidth (560 GB/s vs 360 GB/s per TechPowerUp), but SYCL and Vulkan don't yet turn that into higher generation speed. - No published gpt-oss 20B run on the A770 was in the SpecPicks database as of November 2026, so the A770 side of the model-specific comparison is an estimate.
Part of the RTX 3060 12GB local-LLM guide → Which models fit in 12 GB, each with its cited tok/s, and where two cards or RAM offload take over.
Dense 20-27B models need 14.3–16.7 GB for their Q4 weights; on the Arc A770, the largest Q4 files in the class (16.7 GB) do not fit, but the smaller ones load: 24B at Q4 with a short context; 27B needs Q3 or offload. Arc A770 carries 16 GB of VRAM. The weights column is the range of real Q4_K_M
files on Hugging Face for the models in each size class (about 0.6 GB per billion parameters),
and the runtime plus a usable context window wants about 2 GB on top; every tokens-per-second figure is a
median over community-reported Q4 runs SpecPicks tracks for this card, with the run count and
the source beside it.
Showing the model sizes this article covers and the band either side. Every size from 3B to 70B+, for every card SpecPicks tracks, is in the local-LLM GPU table.
Model size
Weights at Q4
Fits in 16 GB?
Measured
Left for context
Source
12-14B (Qwen 3 14B, Phi-4)Where 8 GB stops being enough. 12 GB is the smallest card that holds it with a usable context window.
20-27B (Gemma 3 27B, Mistral Small)A 27B Q4_K_M file (about 16.7 GB) is more than a 16 GB card holds, so 16 GB means a Q3 quant or partial CPU offload; a 24B loads with a short context. 20 GB holds the class with a usable context window, 24 GB with a long one.
14.3–16.7 GB
Partly: 24B at Q4 with a short context; 27B needs Q3 or offload
gpt-oss ships natively in MXFP4, a 4-bit format applied to the expert weights, so most community "quants" stay close to the native file size. The table lists the native file size where one is published, and labels anything else as arithmetic.
On these cards, the only realistic option is the native MXFP4 file. Re-quantizing to a "larger" format just makes it bigger without meaningfully improving quality, because the model was trained with MXFP4 experts.
Spec delta: Arc A770 16GB vs RTX 3060 12GB
Spec
Arc A770 16GB
RTX 3060 12GB
Why it matters
Source
VRAM
16 GB GDDR6, 256-bit
12 GB GDDR6, 192-bit
Decides whether gpt-oss 20B plus long context stays on the GPU
The model-specific figure comes from one owner report. The broader comparison uses medians from the SpecPicks benchmark database as of November 2026, each aggregating published third-party runs.
On models that fit in both cards, the 3060 leads by roughly 50% at 7-9B and 60% at 12-15B. The A770's spread is wide, though. Per the llama.cpp SYCL performance discussion, Llama 2 7B Q4_0 runs at 59-67 tok/s on the A770, while some Ollama and IPEX-LLM runs of newer 8B models land under 25 tok/s. Backend choice moves Arc results more than anything else.
What about gpt-oss 20B on the A770? gpt-oss activates only 3.6B parameters per token, about 2 GB of MXFP4 weights read per generated token. On paper, the A770's 560 GB/s gives it a higher bandwidth ceiling than the 3060. In practice, Arc's backends reach a smaller share of their theoretical bandwidth than CUDA does, as the 7-9B medians show. A reasonable expectation is that the A770 lands below the 3060 at short context and holds its speed better as context grows, because it never offloads. Treat that as a projection until a published run exists.
Prefill vs generation
Prompt processing (prefill) is compute-bound, and this is where CUDA's maturity shows most clearly. On the llama.cpp scoreboards for Llama 2 7B Q4_0:
Arc A770 (SYCL): about 940 tok/s prefill, per the SYCL discussion.
Arc A770 (Vulkan): from about 310 to 1,120 tok/s depending on driver and build, per the Vulkan scoreboard.
For chat, where prompts are short, prefill barely matters. For RAG, coding agents and long-document summarization, where you feed thousands of tokens per request, the 3060's two-times-plus prefill advantage cuts time to first token sharply. That narrows the A770's long-context edge, because long contexts are exactly where prefill time adds up.
Context-length impact: KV cache on 16 GB vs 12 GB
gpt-oss 20B keeps its KV cache small. Per the model's config, it has 24 layers that alternate between full attention and a 128-token sliding window, with 8 KV heads of dimension 64. Only the 12 full-attention layers grow with context. At FP16, that's about 24 KB per token (12 layers × 2 for K and V × 8 heads × 64 × 2 bytes), so the estimates are:
Context
Approx. KV cache (FP16)
A770 16GB (11.27 GiB weights)
RTX 3060 12GB (11.27 GiB weights)
8K
about 0.2 GB
Fits easily
Tight, depends on compute buffers
32K
about 0.8 GB
Fits
Needs expert offload
64K
about 1.6 GB
Fits
Needs more expert offload
The KV cache isn't what breaks the 3060. The problem is that 11.27 GiB of weights leaves under 1 GB on a 12 GB card for the cache, compute buffers and the desktop. The 16K-context owner report shows the result: moving two expert layers to the CPU kept speed at 64 tok/s, a modest 15% drop thanks to the MoE design. Push to 32-64K and more layers move, with steeper losses. The A770 holds the full model plus a 64K context without any offload.
llama.cpp SYCL. Usually the fastest Arc path for generation, and actively maintained upstream. It requires Intel's oneAPI runtime.
llama.cpp Vulkan. The easiest setup, with no oneAPI needed. Performance swings with driver versions, as the scoreboard spread above shows.
IPEX-LLM Ollama builds. Convenient if you're already set up, but they trail upstream Ollama's model support now that development has slowed.
OpenVINO. Strong INT4 numbers on some models in community benchmarks, but a separate model-conversion workflow.
Make sure the backend you choose supports gpt-oss's MXFP4 format and attention sinks before you download. Upstream llama.cpp added support at launch. Forks and older builds may lag.
Perf-per-dollar and perf-per-watt at launch MSRP
TechPowerUp lists launch prices of $349 for the A770 16GB and $329 for the RTX 3060 12GB. Using the 7-9B medians:
RTX 3060: 59.5 tok/s at launch price is about 18 tok/s per $100. At 170 W, about 0.35 tok/s per watt.
Arc A770 16GB: 38.7 tok/s at launch price is about 11 tok/s per $100. At 225 W, about 0.17 tok/s per watt.
The 3060 wins both ratios clearly on models that fit in 12 GB. The A770's case rests entirely on capacity. Current street prices differ from launch, so check the live price buttons.
Common pitfalls
Resizable BAR off. Arc cards lose a lot of performance without ReBAR enabled in the BIOS. Older platforms without it are a poor fit for the A770.
Mixing oneAPI versions. SYCL builds are sensitive to the installed oneAPI runtime. Match the version the build expects.
Assuming Ollama detects Arc. Stock Ollama targets CUDA and ROCm. Arc needs a Vulkan-enabled or Intel-specific build.
Downloading a "Q8" gpt-oss GGUF for quality. It's nearly twice the size, and the experts were trained in MXFP4 anyway.
Verdict matrix
Get the A770 16GB if… you regularly run gpt-oss 20B at 32K-plus context, want headroom for 14B models at Q6-Q8, or plan to run SDXL at higher resolutions, and you're comfortable maintaining a SYCL or Vulkan setup.
Get the RTX 3060 12GB if… you run gpt-oss 20B for chat and coding at typical context lengths, want Ollama and LM Studio to work out of the box, or care about prefill speed for RAG. It's faster on every model that fits in 12 GB.
Recommended pick
Buy the MSI RTX 3060 12GB for most gpt-oss 20B use. It runs the native MXFP4 model fully on the GPU at short context, loses only about 15% with light expert offload at 16K, and leads the A770 by roughly 50% on 7-9B models. Choose the ASRock Challenger Arc A770 16GB only if long-context work is your daily workload and you value never offloading over setup simplicity.
This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.
Small-model (7–9B) reference throughput
These figures are for 7–9B models, not the 20–27B models this article covers. The table holds model size fixed so the cards can be compared with each other; for the size in the title, use the article's own figures.
Median generation throughput at 7–9B models (Llama 3.1 8B, Qwen 3 8B), Q4 quantization, from community-reported runs SpecPicks tracks. Each row pools runs from different sources, runtimes and models in that class, so the rows are not a matched head-to-head; where the article compares cards on the same rig, its own figures are the like-for-like result. Street price is the second-lowest listing priced within the last 24 hours at 0.5–2× launch MSRP, so no single listing sets it, and a street figure well above MSRP says so in its cell; where too few listings pass that check the cell shows a dash and the launch MSRP, which is not comparable with the street figures. Rows marked for comparison are not the cards this article is about — they are included so the throughput column has something to be read against.
As an Amazon Associate, SpecPicks earns from qualifying purchases; we also earn on qualifying eBay purchases via the eBay Partner Network. Prices shown were last tracked at crawl time and may vary — check the listing for the current price.
Does Ollama support the Intel Arc A770 out of the box?
Not through its default CUDA/ROCm builds. Arc users typically run llama.cpp with the SYCL or Vulkan backend, or an IPEX-LLM build of Ollama. Intel has wound down BigDL, so the llama.cpp SYCL and Vulkan paths are the most actively maintained routes today. Expect more setup work than an NVIDIA card, where Ollama detects the GPU automatically.
Why does 16 GB matter so much for gpt-oss 20B?
The ggml-org MXFP4 GGUF of gpt-oss 20B is 11.27 GiB, per an owner report in llama.cpp discussion #15396. That fits a 12 GB card at a 2K context, where an RTX 3060 reached 75 tok/s, but leaves under 1 GB for the KV cache and compute buffers. By 16K context the 3060 needs expert-layer offload. A 16 GB card holds the weights plus a 64K context with no offload.
Is the RTX 3060 still the safer buy for beginners?
Yes, for software simplicity. CUDA support across Ollama, LM Studio, vLLM and ComfyUI is mature, and the 3060's 7-9B median of 59.5 tok/s across 43 published runs is well ahead of the A770's 38.7 tok/s on the SpecPicks benchmark pages. The A770 wins only when a model plus its context needs more than 12 GB, such as gpt-oss 20B at 32K-plus context.
What PSU do I need for either card?
Per TechPowerUp's spec database, the Arc A770 is rated around 225 W and the RTX 3060 around 170 W. A quality 550-650 W unit covers either card in a typical single-GPU desktop with headroom for transients. Check that your PSU has the right number of 8-pin PCIe connectors, since some A770 boards use two.
Will the Arc A770 also handle image generation?
It can run Stable Diffusion and SDXL through OpenVINO, IPEX or ComfyUI's Intel paths, and 16 GB of VRAM helps with SDXL resolutions. Tooling still lags CUDA, though, and some custom nodes and extensions assume NVIDIA. If image generation is your main workload rather than LLM chat, the RTX 3060's broader compatibility usually outweighs the A770's extra memory.
