Reflection AI, United States · Released October 16, 2026Beam 1.0 501B-A23B at a glance
- Made by
- Reflection AI, United States
- Released
- October 16, 2026
- Parameters
- 501.3B, 23.4B active
- Context window
- 1M tokens
Beam 1.0 501B-A23B VRAM Requirements
Reflection AI, United States · Released October 16, 2026New
Beam 1.0 501B-A23B has 501.3B parameters (23.4B active per token). In BF16 its weights alone take 934 GiB; quantized to 4-bit, about 282 GiB. Each 32K-token request adds 1.21 GiB of KV cache. Cheapest way to run it today: 7 × RTX A6000 (48 GB) at $2.25 per hour.
Size your own setup in the calculator →VRAM by precision and context length
Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.
| Weights format | Weights only | + 8K context | + 32K context | + 128K context | + 1M context |
|---|---|---|---|---|---|
| NVFP4 experts + BF16 (native) | 273 | 274 | 275 | 278 | 310 |
| BF16 / FP16 | 934 | 934 | 935 | 938 | 970 |
| FP8 / INT8 | 468 | 469 | 470 | 473 | 504 |
| 4-bit (GGUF Q4_K_M) | 282 | 282 | 283 | 287 | 318 |
How many GPUs to run Beam 1.0 501B-A23B
Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.
| GPU | NVFP4 experts + BF16 (native) | BF16 / FP16 | 8-bit | 4-bit |
|---|---|---|---|---|
| GeForce RTX 4090 24 GB | Not on this GPU | 48 (several machines) | 24 (several machines) | 16 (several machines) |
| GeForce RTX 5090 32 GB | Not on this GPU | 48 (several machines) | 24 (several machines) | 16 (several machines) |
| RTX PRO 6000 Blackwell 96 GB | Not on this GPU | 16 (several machines) | 6 | 4 |
| Mac M5 Max 128 GB 96 GB usable | Not on this GPU | 16 (several machines) | 6 | 3 |
| Ryzen AI Max+ 395 128 GB 96 GB usable | Not on this GPU | 16 (several machines) | 6 | 3 |
| DGX Spark (GB10) 128 GB | Not on this GPU | 8 | 4 | 3 |
| NVIDIA H100 80 GB | Not on this GPU | 16 (several servers) | 8 | 5 |
| NVIDIA H200 141 GB | Not on this GPU | 8 | 4 | 3 |
| NVIDIA B200 (HGX) 180 GB | 2 | 7 | 4 | 2 |
8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size. Not on this GPU: the official NVFP4 experts + BF16 weights do not run there (llama.cpp needs a GGUF version, and vLLM needs a GPU with FP4 support).
Cheapest way to run Beam 1.0 501B-A23B today
For each GPU: the fewest cards that fit Beam 1.0 501B-A23B with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-17).
| Setup | Per hour | Weights | Provider | Per month | Rent |
|---|---|---|---|---|---|
| 7 × RTX A6000 48 GB | $2.25 | Q4_K_M | Vast.ai | $1,640.31 | Rent 7 × RTX A6000 on Vast.ai (opens in a new tab) → |
| 8 × NVIDIA A40 48 GB | $3.22 | 4-bit | Vast.ai | $2,347.68 | Rent 8 × NVIDIA A40 on Vast.ai (opens in a new tab) → |
| 8 × NVIDIA L40S 48 GB | $3.43 | 4-bit | Vast.ai | $2,505.36 | Rent 8 × NVIDIA L40S on Vast.ai (opens in a new tab) → |
| 7 × RTX 6000 Ada 48 GB | $3.95 | Q4_K_M | Vast.ai | $2,882.04 | Rent 7 × RTX 6000 Ada on Vast.ai (opens in a new tab) → |
| 8 × NVIDIA L40 48 GB | $3.95 | 4-bit | Vast.ai | $2,884.96 | Rent 8 × NVIDIA L40 on Vast.ai (opens in a new tab) → |
| 2 × AMD MI300X 192 GB | $4.78 | 4-bit | RunPod | $3,489.40 | Rent 2 × AMD MI300X on RunPod (opens in a new tab) → |
| 4 × RTX PRO 6000 Blackwell 96 GB | $5.07 | Q4_K_M | Vast.ai | $3,699.64 | Rent 4 × RTX PRO 6000 Blackwell on Vast.ai (opens in a new tab) → |
| 5 × NVIDIA A100 80GB | $5.34 | 4-bit | Vast.ai | $3,898.20 | Rent 5 × NVIDIA A100 80GB on Vast.ai (opens in a new tab) → |
On-demand prices from AWS, Google Cloud, Azure, RunPod, Vast.ai, Oracle Cloud, OVHcloud, Scaleway, Verda, Akash and UpCloud, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.
AWS, Google Cloud and UpCloud prices: Spare Cores (opens in a new tab), CC BY-SA 4.0 (opens in a new tab), shared under the same license.
KV cache: how memory grows with context
Architecture: MoE 256 experts · 9 full-attention layers + 43 sliding-window layers (512 tokens), gated GQA 8 KV × 128. Its KV cache is 5.4× smaller than if every layer used full attention, because most layers only keep a short window of recent tokens. Only 23.4B parameters are active per token, but all 501B must sit in memory: VRAM depends on the total, speed on the active part.
| Context per request | BF16 cache | FP8 cache |
|---|---|---|
| 8K tokens | 0.37 GiB | 0.18 GiB |
| 32K tokens | 1.21 GiB | 0.60 GiB |
| 128K tokens | 4.58 GiB | 2.29 GiB |
| 1M tokens | 36.1 GiB | 18.0 GiB |
Beam 1.0 501B-A23B VRAM FAQ
How much VRAM does Beam 1.0 501B-A23B need?
In BF16 the weights alone take 934 GiB (1003 GB). In FP8 that is 468 GiB, and about 282 GiB with 4-bit quantization (Q4_K_M); the official NVFP4 experts + BF16 checkpoint is 273 GiB. Each request then adds KV cache: 0.37 GiB at 8K tokens and 1.21 GiB at 32K (BF16 cache).
Can Beam 1.0 501B-A23B run on a single RTX 4090 (24 GB)?
No. Even in 4-bit it needs 16 RTX 4090s, spread over several machines. In 4-bit it needs 5 H100 GPUs.
Can Beam 1.0 501B-A23B run on an RTX 5090 (32 GB)?
No. Even in 4-bit it needs 16 RTX 5090s, spread over several machines.
Can Beam 1.0 501B-A23B run on a Mac?
Yes. In 4-bit (Q4_K_M with llama.cpp) it needs a Mac with 512 GB of unified memory, of which macOS gives the GPU about 384 GB, for one request with an 8K-token context. In 8-bit it does not fit on a single Mac.
Can Beam 1.0 501B-A23B run on a DGX Spark or a Ryzen AI Max+ 395 (128 GB)?
No. Even in 4-bit it needs 3 DGX Spark units or 3 Ryzen AI Max+ 395 machines (128 GB), linked over the network.
What is the cheapest way to run Beam 1.0 501B-A23B?
On 2026-10-17, the cheapest on-demand setup is 7 × RTX A6000 (48 GB) with Q4_K_M weights on Vast.ai, at $2.25 per hour (about $1,640.31 per month). Next: 8 × NVIDIA A40 (48 GB) with 4-bit weights on Vast.ai, at $3.22 per hour. Sized for one 8K-token request; prices are checked every hour.
How many H100 GPUs does Beam 1.0 501B-A23B need?
With one request and an 8K-token context: 16 in BF16, 8 in FP8 and 5 in 4-bit (vLLM, 90% of memory usable). Above 8 GPUs, that means several servers. Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.
Beam 1.0 501B-A23B VRAM badge
For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/beam-1-0.json.
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Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.