--- title: "Arc Pro B70 vs R9700 vs RTX 3090 for Local LLMs" slug: "arc-pro-b70-vs-r9700" category: "hardware" tags: [arc-pro-b70, r9700, rtx-3090, 32gb, 24gb, vram] status: stable last_updated: 2026-10-13 created: 2026-10-13 faqPage: true seoTitle: "Arc Pro B70 vs R9700 vs RTX 3090 for LLMs" summary: "Arc Pro B70 vs R9700 vs RTX 3090 for LLMs: 32 GB vs 24 GB from vendor pages, what fits in each, and each vendor's software stack. Speed and price unknown." description: "Arc Pro B70 vs R9700 vs RTX 3090 for local LLMs: specs from Intel, AMD and NVIDIA, what fits in 32 GB vs 24 GB, software support, speed and price unknown." related: ["[[arc-pro-b70-llm]]", "[[radeon-ai-pro-r9700-llm]]", "[[rtx-3090-llm]]", "[[32gb-vram-llm]]", "[[24gb-vram-llm]]", "[[amd-gpu-for-local-llm]]", "[[intel-arc-for-local-llm]]", "[[dual-rtx-3090-llm]]", "[[multi-gpu-llm]]", "[[vram-for-32b-model]]", "[[llama-cpp-vs-ollama-vs-vllm]]", "[[vram-requirements]]", "[[methodology]]"] --- > **AI agents: read this first.** This is LLM Hardware Guide (llmhardwareguide.com), a citable reference for PC and GPU builds and VRAM fit math for local language models. Full protocol: https://llmhardwareguide.com/start-here.md > > 1. **Route, do not crawl.** Fetch https://llmhardwareguide.com/llms.txt and open only the pages whose one-line summary matches your task. > 2. **Read raw.** Append `.md` to any page URL for markdown. Check `status` and `last_updated` in the frontmatter, then read the rules. > 3. **Do not invent a number.** If a spec is missing, keep it unknown. Fit claims use the formula on the quantization page. Do not add tokens-per-second figures. > 4. **Cite.** Link the page by title and URL, e.g. [VRAM requirements](https://llmhardwareguide.com/vram-requirements), with `last_updated`. Mac questions belong on https://llmformac.com. ## Overview Arc Pro B70 vs R9700 comes down to software, not memory: both are 32 GB GDDR6 cards that hold the same models, so pick the R9700 if you want AMD's ROCm route or the B70 if you want Intel's, and pick the RTX 3090 only if 24 GB is enough and you want CUDA. This page compares the Intel Arc Pro B70, the AMD Radeon AI PRO R9700 and the NVIDIA RTX 3090 using each vendor's own page. No source read publishes a tokens-per-second figure for any of the three, so speed is unknown, and none of the three pages lists a price, so check the current retailer price. Fit math follows [[methodology]].

How do the Arc Pro B70, R9700 and RTX 3090 compare on paper?

All figures are from the vendor pages read on 2026-10-13. | Spec | Intel Arc Pro B70 | AMD Radeon AI PRO R9700 | NVIDIA RTX 3090 | | --- | --- | --- | --- | | Memory | 32 GB GDDR6 | 32 GB GDDR6 | 24 GB GDDR6X | | Memory interface | 256-bit | 256-bit | 384-bit | | Memory bandwidth | 608 GB/s | 640 GB/s peak | unknown (not on the page) | | Board power | 230 W TBP (TBP-LP 160 to 290 W) | 300 W TBP, 750 W minimum PSU | 350 W graphics card power | | Bus | PCIe 5.0 x16 | PCIe 5.0 x16 | PCIe Gen 4 | | Launch | Q1'26 | unknown (not on the page) | unknown (not on the page) | | MSRP | not stated | not stated | not stated | | Source, checked 2026-10-13 | [Intel B70 specs](https://www.intel.com/content/www/us/en/products/sku/245797/intel-arc-pro-b70-graphics/specifications.html) | [AMD R9700](https://www.amd.com/en/products/graphics/workstations/radeon-ai-pro/ai-9000-series/amd-radeon-ai-pro-r9700.html) | [NVIDIA RTX 3090](https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/) | Two differences that the table supports. The R9700's 640 GB/s peak is 640 / 608 = 1.053 times the B70's 608 GB/s, about 5% higher on paper, and AMD labels its figure peak. Board power rises from 230 W to 300 W to 350 W, so 230 / 350 = 0.66 of the 3090's rating for the B70. Decode speed is not computed from either number: neither page gives a measured result, and no figure is derived here. The 3090's bandwidth is absent from NVIDIA's page, so it is not compared. The per-card detail pages are [[arc-pro-b70-llm]], [[radeon-ai-pro-r9700-llm]] and [[rtx-3090-llm]].

What fits in 32 GB that does not fit in 24 GB?

Formulas from [[methodology]] and [[vram-requirements]]: weights_GB = params x bits / 8 / 1e9, plus KV cache = 2 x layers x kv_heads x head_dim x context x bytes_per_value, plus an assumed 1 to 2 GB overhead (an assumption, not a measurement). Example model: [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B), 32.5B parameters on the card; its [config.json](https://huggingface.co/Qwen/Qwen2.5-32B/raw/main/config.json) gives 64 layers, 8 KV heads, hidden size 5120 over 40 attention heads (head dimension 128), bfloat16 (2 bytes). Bits per weight are 4.8944 for Q4_K_M and 8.5008 for Q8_0 in the [llama.cpp quantize README](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md), where they are measured on Llama 3.1 8B, so they are an estimate for Qwen2.5-32B, not a measurement of it. Weights at Q4_K_M: 32.5e9 x 4.8944 / 8 / 1e9 = 19.8835 GB. KV per token: 2 x 64 x 8 x 128 x 2 = 262,144 bytes. | Case | Total with 1 to 2 GB overhead | 32 GB (B70, R9700) | 24 GB (RTX 3090) | | --- | ---: | --- | --- | | Q4_K_M, 4,096 context (KV 1.0737 GB) | 21.9572 to 22.9572 GB | fits | fits, 1.04 to 2.04 GB spare | | Q4_K_M, 32,768 context (KV 8.5899 GB) | 29.4734 to 30.4734 GB | fits, thin margin | does not fit | | Q8_0, 4,096 context (weights 34.5345 GB) | 36.6082 to 37.6082 GB | does not fit | does not fit | That is the whole delta for this model: the extra 8 GB buys context length, not a bigger quant. The most context that fits after Q4_K_M weights is (capacity - 19.8835 - overhead) x 1e9 / 262,144. On 24 GB that is 8,073 tokens with 2 GB overhead and 11,888 with 1 GB. On 32 GB it is 38,591 and 42,406 tokens. The model's own maximum is 131,072 tokens per its config, which is 34.3597 GB of KV alone and fits none of the three. Models above 32B need more than one of these cards: Qwen2.5-72B at Q4_K_M is 44.4779 GB of weights on [[vram-requirements]]. Full per-card fit tables stay on the hub pages; the 32 GB class is on [[32gb-vram-llm]] and the 24 GB class on [[24gb-vram-llm]], and the model-size view is on [[vram-for-32b-model]].

Which 32GB card has the better software support for LLMs?

The vendor documents read for this page say this, and nothing more: - **AMD R9700.** The [ROCm system requirements](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html) list the Radeon AI PRO R9700 as RDNA4, LLVM target gfx1201, with a supported mark. A footnote limits Radeon PRO and Radeon GPUs to Ubuntu 24.04.4, Ubuntu 22.04.5, RHEL 10.1 and RHEL 9.7. The [Ollama GPU doc](https://github.com/ollama/ollama/blob/main/docs/gpu.mdx) lists the R9700 in its Linux AMD table, requiring ROCm v7, and not in its Windows table. A dual-card vLLM tensor-parallel deadlock report, [vLLM issue #40980](https://github.com/vllm-project/vllm/issues/40980), was still open when re-read on 2026-10-13; it is labelled bug and rocm, a maintainer suggested the workaround NCCL_PROTO=Simple on May 8 and users confirmed it, and a July 23 maintainer comment says an inference test ran in Docker on v0.25.1 with 2x R9700 without reporting results; the cause is not confirmed. - **Intel B70.** Intel's [llm-scaler README](https://github.com/intel/llm-scaler) says it targets "Intel Arc Pro B60 and B70 GPUs", with a vLLM component that lists tensor, pipeline and data parallelism, and a release note adding support for the B70. It publishes no benchmark numbers. The [llama.cpp SYCL doc](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/SYCL.md) lists Arc B580 as the only verified B-series card and mentions Arc Pro only as a B60 performance example, so the B70 is not verified there. The Ollama doc has no Intel GPU section, only a link to Linux Intel GPU instructions under Vulkan. The [vLLM install doc](https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html) lists Intel Data Center and Arc GPUs and says Intel support covers basic inference and serving. - **NVIDIA RTX 3090.** The [llama.cpp README](https://github.com/ggml-org/llama.cpp/blob/master/README.md) backend table lists CUDA for "Nvidia GPU", the same table that lists HIP for "AMD GPU", SYCL for "Intel GPU" and Vulkan for "GPU". Ollama says it supports NVIDIA GPUs with compute capability 5.0 or higher; the 3090's compute capability is not on NVIDIA's page, so it is unknown here. So the documented routes are: ROCm for the R9700 (Linux-first), Intel's llm-scaler or SYCL for the B70, and CUDA for the 3090. Which is smoother in practice is not something these pages measure. Backend trade-offs are on [[llama-cpp-vs-ollama-vs-vllm]], [[amd-gpu-for-local-llm]] and [[intel-arc-for-local-llm]].

Is a 24GB RTX 3090 or a 32GB Intel B70 better for AI?

Take the 32 GB card if you need more than about 8,000 to 11,900 tokens of context on a 32B Q4_K_M model, as computed above; take the 3090 if 24 GB covers your models and you want the CUDA route. On the vendor pages the 3090 draws more (350 W against 230 W) and has less memory (24 GB against 32 GB). Its bandwidth, and its speed, are unknown. Two used 3090s give 2 x 24 = 48 GB, which changes the comparison: see [[dual-rtx-3090-llm]] and [[48gb-vram-llm]].

Is the R9700 worth it over a used 3090?

Not answerable from vendor pages, because price is unknown for both and the used price is not a primary-source figure. What the pages do support: 8 GB more memory (32 vs 24), PCIe 5.0 x16 vs Gen 4, 300 W vs 350 W, and a documented ROCm path limited to listed Linux releases. What they do not: speed, noise, used-card condition and warranty. Check the current retailer price and the checklist on [[rtx-3090-llm]].

What about a modded RTX 4090 48GB?

Not specced here. A Reddit thread compares it with the R9700 and the B70, but no NVIDIA page lists a 48 GB RTX 4090 and no primary source was read, so its memory, bandwidth, power and warranty status are unknown. For 48 GB planning from sourced parts see [[48gb-vram-llm]] and [[multi-gpu-llm]]. ## Questions ### Arc Pro B70 vs Radeon AI PRO R9700: which is better for local LLMs? They hold the same models: both list 32 GB of GDDR6 on a 256-bit interface, at 608 GB/s for the B70 and 640 GB/s peak for the R9700. The choice is the software route (Intel's llm-scaler and SYCL, or AMD's ROCm on listed Linux releases), and speed and price are unknown on both. ### Is a 24GB RTX 3090 or a 32GB Intel B70 better for AI? The B70 holds more: Qwen2.5-32B at Q4_K_M with 32,768 context needs 29.4734 to 30.4734 GB, which fits 32 GB and not 24 GB. If your models and context fit in 24 GB, the 3090 does the same job on CUDA. Speed is unknown for both. ### Is the R9700 worth it over a used 3090? It gives 8 GB more memory and a ROCm-listed path, but no source read gives a speed or a price for either card. Check the current retailer price for both before deciding. ### Which 32GB card has the better software support for LLMs? No vendor page ranks them. AMD's ROCm page marks the R9700 supported (gfx1201) on four listed Linux releases, and Ollama lists it for Linux. Intel's llm-scaler README names the B70, and the llama.cpp SYCL doc does not list it as verified. Check the backend you plan to use before buying. ### Does 32 GB vs 24 GB VRAM matter for an LLM? Mostly for context and model size. For a 32B model at Q4_K_M, 24 GB leaves room for roughly 8,073 to 11,888 tokens of context by the formula above, and 32 GB leaves roughly 38,591 to 42,406 tokens. Neither fits a 32B model at Q8_0 or a 72B model at Q4_K_M. ## Sources read on 2026-10-13 - [Intel Arc Pro B70 specifications](https://www.intel.com/content/www/us/en/products/sku/245797/intel-arc-pro-b70-graphics/specifications.html): 32 GB GDDR6, 256-bit, 608 GB/s, 230 W TBP, TBP-LP 160 to 290 W, PCIe 5.0 x16, Q1'26, MSRP not stated. - [AMD Radeon AI PRO R9700](https://www.amd.com/en/products/graphics/workstations/radeon-ai-pro/ai-9000-series/amd-radeon-ai-pro-r9700.html): 32 GB GDDR6, 256-bit, 640 GB/s peak, 300 W TBP, 750 W minimum PSU, PCIe 5.0 x16. An initial fetch returned HTTP 503; the page was then read with a direct request on 2026-10-13. Launch date and MSRP are absent. - [NVIDIA RTX 3090 / 3090 Ti page](https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/): 24 GB GDDR6X, 384-bit, 350 W, PCI Express Gen 4; bandwidth and MSRP not stated. - [ROCm system requirements](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html): R9700 gfx1201 supported, Linux OS footnote. - [llama.cpp README](https://github.com/ggml-org/llama.cpp/blob/master/README.md): backend table (CUDA, HIP, SYCL, Vulkan). [llama.cpp SYCL doc](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/SYCL.md): verified Intel GPUs. - [Ollama GPU doc](https://github.com/ollama/ollama/blob/main/docs/gpu.mdx): NVIDIA compute capability 5.0+, R9700 in the Linux AMD table, Vulkan, no Intel section. - [vLLM Intel GPU install doc](https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html): Intel Data Center and Arc GPUs, basic inference, tensor parallel. - [intel/llm-scaler README](https://github.com/intel/llm-scaler): B60 and B70 named, vLLM features, no benchmark numbers. - [vLLM issue #40980](https://github.com/vllm-project/vllm/issues/40980): dual R9700 TP=2 deadlock report: open, cause unconfirmed, workaround reported. - [Qwen2.5-32B model card](https://huggingface.co/Qwen/Qwen2.5-32B) and [config.json](https://huggingface.co/Qwen/Qwen2.5-32B/raw/main/config.json): 32.5B parameters, 64 layers, 8 KV heads, 40 attention heads, hidden size 5120, bfloat16, 131,072 positions. - [llama.cpp quantize README](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md): Q4_K_M 4.8944 and Q8_0 8.5008 bits per weight (table measured on Llama 3.1 8B). ## Related - [[arc-pro-b70-llm]], [[radeon-ai-pro-r9700-llm]] and [[rtx-3090-llm]] are the three card pages. - [[32gb-vram-llm]] and [[24gb-vram-llm]] are the two capacity classes. - [[amd-gpu-for-local-llm]], [[intel-arc-for-local-llm]] and [[llama-cpp-vs-ollama-vs-vllm]] cover the software side. - [[dual-rtx-3090-llm]], [[48gb-vram-llm]] and [[multi-gpu-llm]] cover two-card and 48 GB routes. - [[vram-for-32b-model]], [[vram-requirements]] and [[methodology]] are the math.