EKA-1

EKA-1 is a 26.3-billion-parameter foundation model developed by Soket Labs, unveiled on November 3, 2026, under the IndiaAI Mission. Architected as an open, sparse hybrid Mixture of Experts (MoE) model, EKA-1 was trained from scratch on roughly 7.5 trillion tokens. Its pre-training corpus places heavy emphasis on mathematical reasoning, programming, and agentic workflows across English and all 22 scheduled Indian languages listed under the Eighth Schedule of the Indian Constitution.

Unlike typical cloud-first foundation models, Soket Labs designed EKA-1 specifically for on-premises deployment, private infrastructure, and air-gapped environments. It directly targets sectors where operational data cannot leave internal network boundaries, specifically banking, financial services, insurance (BFSI), and cybersecurity. For practitioners evaluating a local AI model 26.3B parameters 2026 release, EKA-1 occupies a practical middle tier between smaller edge models (under 10B) and resource-intensive 70B+ enterprise models.

By leveraging an MoE design, the model reduces per-token compute demands during generation while preserving the representational capacity of a 26B-scale network. This makes it an attractive candidate for private workstation deployments where deterministic output, local verification, and native Indic language support are strict operational requirements.

EKA-1 utilizes a sparse hybrid Mixture of Experts architecture totaling 26.3B parameters. While Soket Labs has not publicly broken down the exact number of active parameters routed per forward pass, sparse gating ensures that each token engages only a subset of the total network.

In local inference, this design provides a distinct performance profile:

• Memory footprint: The full parameter weight (26.3B) must reside completely in VRAM or unified system memory to avoid disk swapping penalties.
• Compute throughput: Because only a fraction of experts fire per token, floating-point operations (FLOPs) per token are noticeably lower than a standard 26B-32B dense model. This translates to higher generation speeds on memory-bandwidth-constrained consumer hardware.

Soket Labs trained the network on approximately 7.5 trillion tokens, an unusually high token-to-parameter ratio (roughly 285 tokens per parameter) that ensures dense knowledge encoding and deep convergence. The model is text-only, omitting native vision or audio encoders to dedicate parameter capacity strictly to text generation, multi-step logic, code execution paths, and structured tool calling.

While official documentation has not formally standardized the maximum native context window, the model was built from the ground up for agentic execution and API tool invocation. EKA-1 MoE efficiency relies heavily on optimized KV cache implementations (such as FlashAttention-2 or vLLM PagedAttention) to maintain stability when handling multi-turn system prompts and multi-document retrieval tasks.

EKA-1 is tuned for structured tasks where incorrect outputs carry significant business and compliance costs. Its primary capabilities center on reasoning, mathematics, code syntax generation, and multi-lingual processing.

Standard Western and Chinese open models suffer from severe token fragmentation and degraded reasoning in Indic languages due to tokenizer bias. EKA-1 provides native, high-efficiency tokenization across all 22 scheduled Indian languages, including Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, and Odia. This allows practitioners to process unstructured regional documents, regional legal filings, and vernacular customer communications locally without high token overhead or translated degradation.

In a Security Operations Center (SOC) running without outbound internet connectivity, EKA-1 can act as a local analytical copilot. It can parse raw firewall logs, interpret SIEM alerts, correlate network indicators of compromise (IOCs), and generate remediation scripts in Python or Bash without transmitting proprietary infrastructure topologies to external APIs.

The model's training emphasizes step-by-step arithmetic and verifiable deduction. In banking environments, it can verify financial reconciliations, parse local regulatory directives from institutions like the Reserve Bank of India, and cross-reference balance sheets against loan criteria using structured function calls.

With native function-calling and tool-use capabilities, EKA-1 for coding and automated pipelines can execute deterministic routines: querying local SQL databases, generating API payloads, validating schema structures, and orchestrating internal workflows without cloud dependencies.

To run EKA-1 locally, hardware planning must account for both the base model weights and the operational KV cache. Because all 26.3B parameters must sit in memory, running unquantized weights requires enterprise-grade hardware, while quantized variants fit smoothly onto modern consumer cards.

• Best GPU for EKA-1: A single Nvidia GeForce RTX 4090 (24GB) or RTX 3090 (24GB). Both cards comfortably host the model at Q4_K_M or Q5_K_M quantization while leaving 4GB to 8GB of VRAM free for context caching and batch processing.
• Dual-Card Workstations: Systems running dual RTX 3090s or RTX 4090s (48GB total VRAM via tensor parallelism or layer offloading) can run Q8_0 or unquantized BF16 with low latency.
• Apple Silicon: An Apple Mac Studio or MacBook Pro equipped with an M3 Max or M4 Max (36GB unified memory minimum, 64GB+ recommended) runs the Q4_K_M and Q5_K_M versions efficiently with full memory offload.

The best quantization for EKA-1 is Q4_K_M. In typical 26B-scale deployments, 4-bit medium k-quantization retains over 98% of the base model's reasoning performance while fitting within the accessible 24GB VRAM envelope. For deployments where coding and mathematical accuracy cannot tolerate any precision loss, Q5_K_M is the safest balance if memory overhead allows.

On a single RTX 4090 utilizing optimized runtimes such as llama.cpp or vLLM:

• EKA-1 tokens per second: Expect roughly 35 to 55 tokens per second at Q4_K_M for single-batch prompt processing, depending on the active parameter configuration. Because sparse experts bypass inactive parameters, generation speed is noticeably higher than a dense 27B-32B model running on the same GPU.

The fastest way to deploy EKA-1 on a local machine once the weights are published is through Ollama or standard llama.cpp bindings:

1. Ensure your system has the proper CUDA or Metal drivers installed and a minimum of 24GB VRAM/unified memory.
2. If pulling an official GGUF model file, create a local Modelfile:

1   FROM ./eka-1-26.3b-q4_k_m.gguf
2   PARAMETER temperature 0.2
3   PARAMETER top_p 0.9
4   PARAMETER stop "<|im_end|>"
5   SYSTEM "You are EKA-1, an on-premise reasoning and coding assistant."

1. Build and launch the model:

1   ollama create eka-1 -f Modelfile
2   ollama run eka-1

Engineers choosing hardware and models in the 25B-35B class usually evaluate either dense generalist options or modular MoEs. Here is how EKA-1 stacks up against the primary alternatives in this parameter bracket.

Qwen 2.5 32B is an established dense benchmark leader for mathematics, reasoning, and code. However, being a dense 32B model, every token requires the full 32 billion parameters to calculate.

• Inference Speed: EKA-1's MoE design achieves higher generation throughput on single consumer cards compared to Qwen 2.5 32B.
• Hardware Footprint: Both require roughly the same VRAM footprint at Q4 quantization (16GB-20GB), but Qwen 2.5 32B scales its compute requirements heavily under long context.
• Language Coverage: While Qwen covers major Asian and Western languages, EKA-1 is purpose-built and trained from scratch on 22 scheduled Indian languages, eliminating the high tokenization costs and parsing failures common in Indic scripts under Qwen.

Google's Gemma 2 27B is another popular dense model that fits into 24GB GPUs at 4-bit and 8-bit precision.

• Architecture: Gemma 2 uses a dense sliding-window attention mechanism, whereas EKA-1 utilizes a sparse hybrid MoE architecture.
• Indic and Specialized Workloads: Gemma 2 excels in mainstream English coding and general knowledge benchmarks, but lacks deep token efficiency in non-Latin scripts. EKA-1 provides a specialized local alternative for organizations in India or global firms handling South Asian enterprise data who need high EKA-1 reasoning benchmark parity alongside low latency and offline autonomy.